CN116522248A - Nucleic acid abnormal data intelligent research and judgment system based on machine learning - Google Patents
Nucleic acid abnormal data intelligent research and judgment system based on machine learning Download PDFInfo
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Abstract
The invention provides an intelligent research and judgment system for abnormal nucleic acid data based on machine learning, which comprises the following steps: the data acquisition processing module is used for performing field type conversion processing according to the acquired original data detected by the nucleic acid amplification instrument to generate processing data; the intelligent research model determining module is used for utilizing 3 candidate decision tree algorithm research models to carry out training evaluation according to the processing data so as to determine an intelligent research model; and the intelligent research model testing module is used for testing the intelligent research model according to the processing data to obtain the research result of the nucleic acid detection data. According to the invention, the intelligent research and judgment system simulates the analysis process of manually detecting and amplifying the nucleic acid by machine learning, and the learning is continuously accumulated, so that the accuracy same as that of manual research and judgment is achieved, the workload of detection personnel can be greatly reduced, and the detection efficiency is improved.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to an intelligent research and judgment system for abnormal nucleic acid data based on machine learning.
Background
The substance for nucleic acid detection is a viral nucleic acid. Nucleic acid detection is to find out whether there is a foreign invading virus in a human respiratory tract specimen, blood or feces. Thus, once detected as "positive" for nucleic acid, the presence of virus in the patient is demonstrated.
The laboratory management system is built, the early monitoring and early warning capability is enhanced, and the laboratory management system is a key measure for preventing the serious risk in the sanitary health field from the source; the machine learning technology is an important technical support for intelligent research and judgment functions in a laboratory management system.
In the process of detecting nucleic acid, the determination of the result of nucleic acid is needed, and this step is usually needed to determine the gene value and amplification curve of the detection result, and the conventional PCR nucleic acid amplification apparatus can directly determine the result of nucleic acid according to the gene value and threshold value of the detection reagent, but involves the determination of amplification curve or needs to be performed manually.
Currently, most of research and judgment of abnormal nucleic acid data are manually judged or are screened by setting rule conditions by using simple sql sentences; rule conditions also require people to summarize experience and form rules to implement; not only the people with medical care related knowledge, but also the people with computer related professions are needed to participate; manually summarizing rule experience may cause a situation of insufficient rule coverage, and if the data change, a situation of misjudgment occurs to the rule condition;
therefore, it is necessary to propose a nucleic acid abnormality data intelligent research and judgment system based on machine learning.
Disclosure of Invention
The invention provides an intelligent research and judgment system for abnormal nucleic acid data based on machine learning, which is used for simulating the analysis process of a manual nucleic acid detection amplification cycle curve by the intelligent research and judgment system through machine learning, and continuously accumulating learning to achieve the same accuracy as that of the manual research and judgment, so that the workload of detection personnel can be greatly reduced, and the detection efficiency is improved.
The invention provides an intelligent research and judgment system for abnormal nucleic acid data based on machine learning, which comprises the following steps:
the data acquisition processing module is used for performing field type conversion processing according to the acquired original data detected by the nucleic acid amplification instrument to generate processing data;
the intelligent research model determining module is used for utilizing 3 candidate decision tree algorithm research models to carry out training evaluation according to the processing data so as to determine an intelligent research model;
and the intelligent research model testing module is used for testing the intelligent research model according to the processing data to obtain the research result of the nucleic acid detection data.
Further, the data acquisition module comprises a data role setting unit, a data splitting unit and a data copying unit;
a data character setting unit for setting data dependent variables and data independent variables based on the processing data, and setting input parameter data and output parameter data of the intelligent research model;
the data splitting unit is used for splitting and generating a processing data training set and a processing data testing set based on the processing data;
the data copying unit is used for copying the data in the processing data training set into a plurality of processing data copying training sets.
Further, the intelligent research and judgment model determining module comprises an intelligent research and judgment flow establishing unit;
the intelligent research and judgment flow establishing unit is used for:
acquiring amplification cycle curve and gene Cycle Threshold (CT) data in the original data;
carrying the amplification cycle curve and the gene cycle threshold data into a preset judgment matrix to judge, and obtaining abnormal type data;
and acquiring fluorescence value data corresponding to the abnormal type data, inputting the fluorescence value data into a preset specific type classifier, and outputting the specific type data of the abnormal type.
Further, the step of carrying the amplification cycle curve and the gene cycle threshold data into a preset judgment matrix to judge comprises the steps of: performing matching comparison judgment according to a preset curve trend database, and judging a normal detection curve if the amplification cycle curve is an S-shaped curve or a negative parallel line; otherwise, judging the abnormal detection curve to obtain corresponding abnormal type data.
Further, the method for judging by bringing the amplification cycle curve and the gene cycle threshold data into a preset judgment matrix further comprises the steps of judging by combining the region where the amplification cycle curve is located, and specifically comprises the following steps: if the first amplification cycle curve represented by the open reading frame gene (O gene) and the second amplification cycle curve represented by the nucleocapsid protein gene (N gene) are both in the negative region, judging as a normal detection curve; otherwise, determining an abnormality detection curve.
Further, the intelligent grinding and judging model determining module further comprises an intelligent grinding and judging model determining unit;
the intelligent research and judgment model determining unit is used for:
based on the processing data replication training set, model training is carried out by utilizing a candidate classification and regression decision tree (CART) algorithm research model, a random forest algorithm research model and an extreme gradient lifting (XGboost) algorithm research model respectively, so as to obtain 3 model training results;
evaluating the training results of the 3 models to obtain a first research model with optimal evaluation results, and determining the first research model as an intelligent research model;
and training and updating the intelligent research and judgment model according to a preset updating period.
Further, the intelligent research and judgment model test module comprises:
based on the processing data test set, testing the intelligent research model to obtain nucleic acid negative research data and abnormal type specific type data, and obtaining research results of nucleic acid detection data.
Further, the intelligent research model test module further includes a research result verification unit, where the research result verification unit is configured to:
performing negative assignment on normal output parameter data output by the intelligent research and judgment model, and performing automatic verification on the negative assignment based on a preset automatic verification template to verify that the negative assignment is a nucleic acid negative research and judgment result;
and (3) carrying out manual verification on the specific type data of the abnormal type output by the intelligent research and judgment model, and verifying the specific type data to be a positive research and judgment result of the nucleic acid.
Further, the system further comprises an intelligent research and judgment model comprehensive evaluation module, wherein the intelligent research and judgment model comprehensive evaluation module is used for comprehensively evaluating the training and application of the intelligent research and judgment model, and updating or replacing the intelligent research and judgment model according to the evaluation result, and specifically comprises the following steps:
based on two evaluation indexes of the accuracy rate and the false alarm rate, performing first evaluation in an intelligent research and judgment model determining link to determine an intelligent research and judgment model;
acquiring the checking error rate of the intelligent judging model according to the checking result data of the judging result checking unit, if the checking error rate is larger than a preset checking error rate threshold, carrying out second evaluation based on the accuracy rate and the false alarm rate and combining three indexes of the checking error rate, and screening again to determine the intelligent judging model; if the accuracy is greater than a preset accuracy threshold, the false alarm rate is less than the preset false alarm rate threshold, and the check error rate is less than the preset check error rate threshold, determining an intelligent judging model; otherwise, updating the candidate decision tree algorithm model, or using one or more combined models including a logistic regression algorithm model, a support vector machine algorithm model and a deep learning model as candidate models of the intelligent research and judgment model.
Further, the system also comprises a disease development trend prediction module for predicting disease development trend and locating high-altitude areas according to the research and judgment result of the nucleic acid detection data, and specifically comprises:
obtaining a nucleic acid positive research result data set in a preset period according to the research result of the nucleic acid detection data; drawing a disease development trend graph and a high-incidence area distribution schematic diagram based on the nucleic acid positive research result data set;
predicting and obtaining the disease development trend after a plurality of preset periods according to a disease development trend graph and a preset trend prediction algorithm; according to the distribution diagram of the high-incidence area, predicting and obtaining a plurality of disease high-incidence areas after a preset period according to a preset expansion simulation prediction model;
setting a plurality of disease development trend early warning conditions and disease high-incidence area expansion early warning conditions, and if the disease development trend triggers the disease development trend early warning conditions or the disease high-incidence area triggers the disease high-incidence area expansion early warning conditions, sending out a plurality of disease early warning grades, and carrying out prevention diagnosis and treatment according to preset prevention diagnosis and treatment measures according to the disease early warning grades.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a machine learning-based intelligent analysis system for abnormal nucleic acid data;
FIG. 2 is a schematic diagram of a data acquisition module of the intelligent research and judgment system for abnormal nucleic acid data based on machine learning;
fig. 3 is a schematic diagram of a determining module of an intelligent research model of the intelligent research system for determining abnormal data of nucleic acid based on machine learning.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The invention provides an intelligent research and judgment system for abnormal nucleic acid data based on machine learning, which is shown in figure 1 and comprises the following steps:
the data acquisition processing module is used for performing field type conversion processing according to the acquired original data detected by the nucleic acid amplification instrument to generate processing data;
the intelligent research model determining module is used for utilizing 3 candidate decision tree algorithm research models to carry out training evaluation according to the processing data so as to determine an intelligent research model;
and the intelligent research model testing module is used for testing the intelligent research model according to the processing data to obtain the research result of the nucleic acid detection data.
The working principle of the technical scheme is as follows: the data acquisition processing module is used for performing field type conversion processing according to the acquired original data detected by the nucleic acid amplification instrument to generate processing data;
the intelligent research model determining module is used for utilizing 3 candidate decision tree algorithm research models to carry out training evaluation according to the processing data so as to determine an intelligent research model;
and the intelligent research model testing module is used for testing the intelligent research model according to the processing data to obtain the research result of the nucleic acid detection data.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the intelligent research and judgment system simulates the analysis process of the nucleic acid detection amplification cycle curve manually through machine learning, and the learning is accumulated continuously, so that the accuracy same as that of manual research and judgment is achieved, the workload of detection personnel can be greatly reduced, and the detection efficiency is improved.
In one embodiment, as shown in fig. 2, the data acquisition module includes a data role setting unit, a data splitting unit, and a data copying unit;
a data character setting unit for setting data dependent variables and data independent variables based on the processing data, and setting input parameter data and output parameter data of the intelligent research model;
the data splitting unit is used for splitting and generating a processing data training set and a processing data testing set based on the processing data;
the data copying unit is used for copying the data in the processing data training set into a plurality of processing data copying training sets.
The working principle of the technical scheme is as follows: the data acquisition module comprises a data role setting unit, a data splitting unit and a data copying unit;
a data character setting unit for setting data dependent variables and data independent variables based on the processing data, and setting input parameter data and output parameter data of the intelligent research model;
the data splitting unit is used for splitting and generating a processing data training set and a processing data testing set based on the processing data;
the data copying unit is used for copying the data in the processing data training set into a plurality of processing data copying training sets.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the data is convenient to further train and test the intelligent research and judgment model through setting, splitting and copying the data.
In one embodiment, as shown in fig. 3, the intelligent grinding and judging model determining module includes an intelligent grinding and judging flow establishing unit;
the intelligent research and judgment flow establishing unit is used for:
acquiring amplification cycle curve and gene Cycle Threshold (CT) data in the original data;
carrying the amplification cycle curve and the gene cycle threshold data into a preset judgment matrix to judge, and obtaining abnormal type data;
and acquiring fluorescence value data corresponding to the abnormal type data, inputting the fluorescence value data into a preset specific type classifier, and outputting the specific type data of the abnormal type.
The working principle of the technical scheme is as follows: the intelligent research and judgment model determining module comprises an intelligent research and judgment flow establishing unit;
the intelligent research and judgment flow establishing unit is used for:
acquiring amplification cycle curve and gene Cycle Threshold (CT) data in the original data;
carrying the amplification cycle curve and the gene cycle threshold data into a preset judgment matrix to judge, and obtaining abnormal type data;
and acquiring fluorescence value data corresponding to the abnormal type data, inputting the fluorescence value data into a preset specific type classifier, and outputting the specific type data of the abnormal type.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, reference is provided for the input and output data of the intelligent research and judgment model through the judgment and the setting of the specific data.
In one embodiment, the bringing of the amplification cycle curve and the gene cycle threshold data into a preset decision matrix for decision comprises: performing matching comparison judgment according to a preset curve trend database, and judging a normal detection curve if the amplification cycle curve is an S-shaped curve or a negative parallel line; otherwise, judging the abnormal detection curve to obtain corresponding abnormal type data.
The working principle of the technical scheme is as follows: bringing the amplification cycle curve and the gene cycle threshold data into a preset judgment matrix to judge, wherein the method comprises the following steps of: performing matching comparison judgment according to a preset curve trend database, and judging a normal detection curve if the amplification cycle curve is an S-shaped curve or a negative parallel line; otherwise, judging the abnormal detection curve to obtain corresponding abnormal type data.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the classification standard and the condition can be provided with reference by judging the normal or abnormal curve form.
In one embodiment, the amplification cycle curve and the gene cycle threshold data are brought into a preset determination matrix to perform determination, and the determination method further includes determining, in combination with a region where the amplification cycle curve is located, specifically including: if the first amplification cycle curve represented by the open reading frame gene (O gene) and the second amplification cycle curve represented by the nucleocapsid protein gene (N gene) are both in the negative region, judging as a normal detection curve; otherwise, determining an abnormality detection curve.
The working principle of the technical scheme is as follows: bringing the amplification cycle curve and the gene cycle threshold data into a preset judgment matrix to judge, wherein the method further comprises the steps of combining the region where the amplification cycle curve is located to judge, and specifically comprises the following steps: if the first amplification cycle curve represented by the open reading frame gene (O gene) and the second amplification cycle curve represented by the nucleocapsid protein gene (N gene) are both in the negative region, judging as a normal detection curve; otherwise, determining an abnormality detection curve.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, whether the curve is normal or not is judged according to different areas where the open reading frame gene and the nucleocapsid protein gene are located.
In one embodiment, the intelligent grinding and judging model determining module further includes an intelligent grinding and judging model determining unit;
the intelligent research and judgment model determining unit is used for:
based on the processing data replication training set, model training is carried out by utilizing a candidate classification and regression decision tree (CART) algorithm research model, a random forest algorithm research model and an extreme gradient lifting (XGboost) algorithm research model respectively, so as to obtain 3 model training results;
evaluating the training results of the 3 models to obtain a first research model with optimal evaluation results, and determining the first research model as an intelligent research model;
and training and updating the intelligent research and judgment model according to a preset updating period.
The working principle of the technical scheme is as follows: the intelligent research model determining module further comprises an intelligent research model determining unit;
the intelligent research and judgment model determining unit is used for:
based on the processing data replication training set, model training is carried out by utilizing a candidate classification and regression decision tree (CART) algorithm research model, a random forest algorithm research model and an extreme gradient lifting (XGboost) algorithm research model respectively, so as to obtain a model training result;
evaluating the training results of the 3 models to obtain a first research model with optimal evaluation results, and determining the first research model as an intelligent research model;
and training and updating the intelligent research and judgment model according to a preset updating period.
In the process of training and updating the intelligent research and judgment model, the acquired subspace matrix and the category characteristic average matrix act together with the characteristic matrix at the stage to finish classification of new samples; however, the situation that data are repeatedly used for many times is necessary in the category with small data volume in the unbalanced data set, and deviation is formed in the process of solving the subspace matrix mean value and the category characteristic matrix mean value, so that the unbalance of the data set is caused, and the performance of the inheritance subspace parameter classifier is influenced; comparing the calculated deviation with a preset deviation threshold value, and if the deviation is larger than the preset deviation threshold value, and if the deviation is at risk of affecting the accuracy of the intelligent research model, re-acquiring the processed data replication training set; the calculation formula of the deviation is as follows:
in the above formula, F is a deviation value, and n is the number of feature matrixes for processing different data of the data replication training set; v 2 Representing the offset of the feature matrix sample points and subspaces of the 2 nd processing data replication training set; v i Representing the offset of the feature matrix sample points and subspaces of the ith processing data replication training set; h 2 A projection matrix representing the sample points of the 2 nd feature matrix,a transpose of the projection matrix representing the 2 nd feature matrix sample points; h i Projection matrix representing the sample points of the ith feature matrix,/->A transpose of the projection matrix representing the ith feature matrix sample points; f (q) represents an objective function of the feature matrix sample, q represents a sample of the classified query set; the term represents a norm operation;
the beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the intelligent research model is trained and updated in time after being screened and determined, so that the application of the intelligent research model is facilitated, and meanwhile, the data reference is provided for ensuring the accuracy of the intelligent research model by calculating the deviation.
In one embodiment, the intelligent research model test module comprises:
based on the processing data test set, testing the intelligent research model to obtain nucleic acid negative research data and abnormal type specific type data, and obtaining research results of nucleic acid detection data.
The working principle of the technical scheme is as follows: the intelligent research and judgment model test module comprises:
based on the processing data test set, testing the intelligent research model to obtain nucleic acid negative research data and abnormal type specific type data, and obtaining research results of nucleic acid detection data.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the research and judgment effect of the intelligent research and judgment model can be checked through testing of the data test set.
In one embodiment, the intelligent research model test module further includes a research result verification unit for:
performing negative assignment on normal output parameter data output by the intelligent research and judgment model, and performing automatic verification on the negative assignment based on a preset automatic verification template to verify that the negative assignment is a nucleic acid negative research and judgment result;
and (3) carrying out manual verification on the specific type data of the abnormal type output by the intelligent research and judgment model, and verifying the specific type data to be a positive research and judgment result of the nucleic acid.
The working principle of the technical scheme is as follows: the intelligent research model test module further comprises a research result verification unit, wherein the research result verification unit is used for:
performing negative assignment on normal output parameter data output by the intelligent research and judgment model, and performing automatic verification on the negative assignment based on a preset automatic verification template to verify that the negative assignment is a nucleic acid negative research and judgment result;
and (3) carrying out manual verification on the specific type data of the abnormal type output by the intelligent research and judgment model, and verifying the specific type data to be a positive research and judgment result of the nucleic acid.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the accuracy of the research and judgment result can be further improved by checking the research and judgment result.
In one embodiment, the system further comprises an intelligent research model comprehensive evaluation module, wherein the intelligent research model comprehensive evaluation module is used for comprehensively evaluating the training and the application of the intelligent research model, and updating or replacing the intelligent research model according to the evaluation result, and specifically comprises the following steps:
based on two evaluation indexes of the accuracy rate and the false alarm rate, performing first evaluation in an intelligent research and judgment model determining link to determine an intelligent research and judgment model;
acquiring the checking error rate of the intelligent judging model according to the checking result data of the judging result checking unit, if the checking error rate is larger than a preset checking error rate threshold, carrying out second evaluation based on the accuracy rate and the false alarm rate and combining three indexes of the checking error rate, and screening again to determine the intelligent judging model; if the accuracy is greater than a preset accuracy threshold, the false alarm rate is less than the preset false alarm rate threshold, and the check error rate is less than the preset check error rate threshold, determining an intelligent judging model; otherwise, updating the candidate decision tree algorithm model, or using one or more combined models including a logistic regression algorithm model, a support vector machine algorithm model and a deep learning model as candidate models of the intelligent research and judgment model.
The working principle of the technical scheme is as follows: the intelligent research model comprehensive evaluation module is used for carrying out comprehensive evaluation by combining training and application of the intelligent research model, and updating or replacing the intelligent research model according to an evaluation result, and specifically comprises the following steps:
based on two evaluation indexes of the accuracy rate and the false alarm rate, performing first evaluation in an intelligent research and judgment model determining link to determine an intelligent research and judgment model;
acquiring the checking error rate of the intelligent judging model according to the checking result data of the judging result checking unit, if the checking error rate is larger than a preset checking error rate threshold, carrying out second evaluation based on the accuracy rate and the false alarm rate and combining three indexes of the checking error rate, and screening again to determine the intelligent judging model; if the accuracy is greater than a preset accuracy threshold, the false alarm rate is less than the preset false alarm rate threshold, and the check error rate is less than the preset check error rate threshold, determining an intelligent judging model; otherwise, updating the candidate decision tree algorithm model, or using one or more combined models including a logistic regression algorithm model, a support vector machine algorithm model and a deep learning model as candidate models of the intelligent research and judgment model.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the comprehensive evaluation is performed by combining the training and the application of the intelligent research and judgment model, so that the evaluation effect can be improved, and the intelligent research and judgment model can be updated or replaced in a targeted manner.
In one embodiment, the system further includes a disease development trend prediction module for predicting a disease development trend and locating a high-altitude area according to a research result of the nucleic acid detection data, and specifically includes:
obtaining a nucleic acid positive research result data set in a preset period according to the research result of the nucleic acid detection data; drawing a disease development trend graph and a high-incidence area distribution schematic diagram based on the nucleic acid positive research result data set;
predicting and obtaining the disease development trend after a plurality of preset periods according to a disease development trend graph and a preset trend prediction algorithm; according to the distribution diagram of the high-incidence area, predicting and obtaining a plurality of disease high-incidence areas after a preset period according to a preset expansion simulation prediction model;
setting a plurality of disease development trend early warning conditions and disease high-incidence area expansion early warning conditions, and if the disease development trend triggers the disease development trend early warning conditions or the disease high-incidence area triggers the disease high-incidence area expansion early warning conditions, sending out a plurality of disease early warning grades, and carrying out prevention diagnosis and treatment according to preset prevention diagnosis and treatment measures according to the disease early warning grades.
The working principle of the technical scheme is as follows: the system also comprises a disease development trend prediction module for predicting disease development trend and locating high-altitude areas according to the research and judgment result of the nucleic acid detection data, and specifically comprises:
obtaining a nucleic acid positive research result data set in a preset period according to the research result of the nucleic acid detection data; drawing a disease development trend graph and a high-incidence area distribution schematic diagram based on the nucleic acid positive research result data set;
predicting and obtaining the disease development trend after a plurality of preset periods according to a disease development trend graph and a preset trend prediction algorithm; according to the distribution diagram of the high-incidence area, predicting and obtaining a plurality of disease high-incidence areas after a preset period according to a preset expansion simulation prediction model;
setting a plurality of disease development trend early warning conditions and disease high-incidence area expansion early warning conditions, and if the disease development trend triggers the disease development trend early warning conditions or the disease high-incidence area triggers the disease high-incidence area expansion early warning conditions, sending out a plurality of disease early warning grades, and carrying out prevention diagnosis and treatment according to preset prevention diagnosis and treatment measures according to the disease early warning grades.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the development trend of the illness state can be predicted and the development situation of the predicted illness state can be comprehensively and systematically mastered by predicting the development trend of the illness state and positioning the high-altitude area according to the research and judgment result of the nucleic acid detection data, and the preventive diagnosis and treatment work can be made in a targeted manner.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The intelligent research and judgment system for the abnormal data of the nucleic acid based on machine learning is characterized by comprising the following components:
the data acquisition processing module is used for performing field type conversion processing according to the acquired original data detected by the nucleic acid amplification instrument to generate processing data;
the intelligent research model determining module is used for utilizing 3 candidate decision tree algorithm research models to carry out training evaluation according to the processing data so as to determine an intelligent research model;
and the intelligent research model testing module is used for testing the intelligent research model according to the processing data to obtain the research result of the nucleic acid detection data.
2. The intelligent research and judgment system for abnormal nucleic acid data based on machine learning according to claim 1, wherein the data acquisition module comprises a data role setting unit, a data splitting unit and a data copying unit;
a data character setting unit for setting data dependent variables and data independent variables based on the processing data, and setting input parameter data and output parameter data of the intelligent research model;
the data splitting unit is used for splitting and generating a processing data training set and a processing data testing set based on the processing data;
the data copying unit is used for copying the data in the processing data training set into a plurality of processing data copying training sets.
3. The intelligent research and judgment system for abnormal nucleic acid data based on machine learning according to claim 1, wherein the intelligent research and judgment model determining module comprises an intelligent research and judgment flow establishing unit;
the intelligent research and judgment flow establishing unit is used for:
acquiring an amplification cycle curve and gene cycle threshold data in original data;
carrying the amplification cycle curve and the gene cycle threshold data into a preset judgment matrix to judge, and obtaining abnormal type data;
and acquiring fluorescence value data corresponding to the abnormal type data, inputting the fluorescence value data into a preset specific type classifier, and outputting the specific type data of the abnormal type.
4. The intelligent research and decision system for abnormal nucleic acid data based on machine learning according to claim 3, wherein the step of bringing the amplification cycle curve and the gene cycle threshold data into a predetermined decision matrix for decision comprises: performing matching comparison judgment according to a preset curve trend database, and judging a normal detection curve if the amplification cycle curve is an S-shaped curve or a negative parallel line; otherwise, judging the abnormal detection curve to obtain corresponding abnormal type data.
5. The intelligent research and judgment system for abnormal nucleic acid data based on machine learning according to claim 3, wherein the amplification cycle curve and the gene cycle threshold data are carried into a preset judgment matrix for judgment, and further comprising, in combination with the area where the amplification cycle curve is located, judging specifically comprising: if the first amplification cycle curve represented by the open reading frame gene and the second amplification cycle curve represented by the nucleocapsid protein gene are both in a negative area, judging as a normal detection curve; otherwise, determining an abnormality detection curve.
6. The intelligent research and judgment system for abnormal nucleic acid data based on machine learning according to claim 1, wherein the intelligent research and judgment model determining module further comprises an intelligent research and judgment model determining unit;
the intelligent research and judgment model determining unit is used for:
based on the processing data replication training set, model training is carried out by utilizing a candidate classification and regression decision tree algorithm research model, a random forest algorithm research model and an extreme gradient lifting algorithm research model respectively, so as to obtain 3 model training results;
evaluating the training results of the 3 models to obtain a first research model with optimal evaluation results, and determining the first research model as an intelligent research model;
and training and updating the intelligent research and judgment model according to a preset updating period.
7. The machine learning based nucleic acid anomaly data intelligent research and judgment system of claim 2, wherein the intelligent research and judgment model test module comprises:
based on the processing data test set, testing the intelligent research model to obtain nucleic acid negative research data and abnormal type specific type data, and obtaining research results of nucleic acid detection data.
8. The intelligent research and judgment system for abnormal nucleic acid data based on machine learning of claim 7, wherein the intelligent research and judgment model test module further comprises a research and judgment result verification unit for:
performing negative assignment on normal output parameter data output by the intelligent research and judgment model, and performing automatic verification on the negative assignment based on a preset automatic verification template to verify that the negative assignment is a nucleic acid negative research and judgment result;
and (3) carrying out manual verification on the specific type data of the abnormal type output by the intelligent research and judgment model, and verifying the specific type data to be a positive research and judgment result of the nucleic acid.
9. The intelligent research and judgment system for abnormal nucleic acid data based on machine learning according to claim 8, further comprising an intelligent research and judgment model comprehensive evaluation module, wherein the intelligent research and judgment model comprehensive evaluation module is used for carrying out comprehensive evaluation in combination with training and application of the intelligent research and judgment model, and updating or replacing the intelligent research and judgment model according to the evaluation result, and specifically comprises:
based on two evaluation indexes of the accuracy rate and the false alarm rate, performing first evaluation in an intelligent research and judgment model determining link to determine an intelligent research and judgment model;
acquiring the checking error rate of the intelligent judging model according to the checking result data of the judging result checking unit, if the checking error rate is larger than a preset checking error rate threshold, carrying out second evaluation based on the accuracy rate and the false alarm rate and combining three indexes of the checking error rate, and screening again to determine the intelligent judging model; if the accuracy is greater than a preset accuracy threshold, the false alarm rate is less than the preset false alarm rate threshold, and the check error rate is less than the preset check error rate threshold, determining an intelligent judging model; otherwise, updating the candidate decision tree algorithm model, or using one or more combined models including a logistic regression algorithm model, a support vector machine algorithm model and a deep learning model as candidate models of the intelligent research and judgment model.
10. The intelligent research and judgment system for abnormal nucleic acid data based on machine learning according to claim 1, further comprising a disease development trend prediction module for predicting disease development trend and locating high-altitude areas according to the research and judgment result of the nucleic acid detection data, and specifically comprising:
obtaining a nucleic acid positive research result data set in a preset period according to the research result of the nucleic acid detection data; drawing a disease development trend graph and a high-incidence area distribution schematic diagram based on the nucleic acid positive research result data set;
predicting and obtaining the disease development trend after a plurality of preset periods according to a disease development trend graph and a preset trend prediction algorithm; according to the distribution diagram of the high-incidence area, predicting and obtaining a plurality of disease high-incidence areas after a preset period according to a preset expansion simulation prediction model;
setting a plurality of disease development trend early warning conditions and disease high-incidence area expansion early warning conditions, and if the disease development trend triggers the disease development trend early warning conditions or the disease high-incidence area triggers the disease high-incidence area expansion early warning conditions, sending out a plurality of disease early warning grades, and carrying out prevention diagnosis and treatment according to preset prevention diagnosis and treatment measures according to the disease early warning grades.
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