CN113764105A - Cardiovascular data prediction method for middle-aged and old people - Google Patents
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Abstract
The invention discloses a prediction method of cardiovascular data of middle and old aged people, when the Spark big data framework based on the elastic distributed data set of a memory is used for data calculation, an intermediate result does not need to be stored in a hard disk, so the calculation speed is higher, and the Spark big data platform can not increase too much economic cost when the price of a memory bank of a computer is reduced. The cardiovascular disease pathological parameter information of middle-aged and old people in the community is stored and managed based on the big data platform, the collection cost of pathological parameter collection of middle-aged and old people in the community is reduced, and meanwhile, the utilization rate of the pathological parameter information is also improved. The prediction model prediction module acquires first data through the data acquisition module, and then the risk of cardiovascular diseases can be calculated through a formula according to the acquired first data; adopt machine learning algorithm to judge the sick risk of middle-aged and old people's cardiovascular disease, guarantee that the old person that can be better is healthy, reduced community doctor's work burden simultaneously.
Description
Technical Field
The invention relates to the technical field of medical risk assessment, in particular to a method for predicting cardiovascular data of middle-aged and old people.
Background
Worldwide, cardiovascular disease is one of the leading causes of death and the burden of disease. The data from the international heart association show that 1700 million people die globally each year from cardiovascular disease, and 80% of these deaths can be effectively prevented by early identification; cardiovascular diseases are common diseases of the old, and with the ever-increasing mouth of the middle-aged and the elderly in China in recent years, the number of patients with sudden cardiovascular diseases in the middle-aged and the elderly population is also increasing continuously. It is well known that ten minutes after a sudden onset of cardiovascular disease is the optimal rescue time, and if timely treatment is not available within these ten minutes, the patient is at great risk of death. In the case of cardiovascular diseases, although the incubation period is long, experienced doctors can find the cardiovascular diseases in time in the early stage of the patients through exercise stress tests, Holter detection, blood pressure measurement and the like of the patients and give corresponding treatments, thereby reducing the possibility of sudden cardiovascular diseases of the patients.
However, the current community medical conditions in China have certain limitations, not all the middle-aged and the elderly in the community can carry out long-term cardiovascular disease risk investigation, and most scientific researchers mainly use collected electronic medical record samples for research work in the disease prediction field, but pathological parameter information obtained by part of hospitals is not easy to collect in the community, so the samples have no pertinence. In addition, the medical data utilization rate of the old people in the community is low due to the fact that the degree of medical informatization of the community is low. In order to solve the problems, a cardiovascular data prediction method for the middle-aged and the elderly needs to be provided to improve the utilization rate of community medical data and reduce the harm of cardiovascular diseases to the health of the middle-aged and the elderly.
Disclosure of Invention
The invention aims to provide a middle-aged and old people cardiovascular data prediction method which can better guarantee the health of middle-aged and old people and simultaneously reduce the workload of community doctors.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for predicting cardiovascular data of middle-aged and old people comprises the following steps:
s1, constructing a data management module of cardiovascular disease pathological parameter information based on Spark SQL components, and uniformly managing, storing and inquiring the pathological parameter information of middle-aged and elderly people received from multiple community hospital clients by using the data management module;
step S2, inputting first data through a data management module, wherein the first data comprises a single selection of sex, a condition of taking antihypertensive drugs, a condition of smoking and a condition of diabetes; inputting specific numerical values in input boxes corresponding to the age and the systolic pressure; identifying the age data and the systolic pressure data after natural logarithm conversion processing by adopting an identification unit; the condition data of taking the antihypertensive drug includes that the taken antihypertensive drug is identified as 1, and the condition data of not taking the antihypertensive drug is identified as 0; identifying smoking as 1 and identifying non-smoking as 0 in the smoking condition data; the diabetes condition data has a diabetes identification of 1 and no diabetes identification of 0;
step S3, in a prediction model prediction module, training a BP neural network by using a preprocessed data set, and judging the cardiovascular disease risk of the middle-aged and the elderly by combining pathological parameter information input by a client based on the trained BP neural network;
and step S4, presenting the prediction result to a user through the result output module.
Preferably, in step S1, the interaction between the system and the user is implemented through the client, the user registers pathological parameter information of the elderly through the client, and the system presents the risk probability of cardiovascular diseases of the elderly and health advice to the user through the client.
Preferably, in step S1, the data preprocessing module is used to preprocess the pathological parameter information in the data set for training the BP neural network.
Preferably, in step S1, based on the Spark SQL component, the data management module includes a HIVE data warehouse, a Spark calculation engine, and an HDFS storage unit, and is configured to perform data query, storage, and unified management on the pathological parameter information received from multiple community hospital clients.
Preferably, the data preprocessing module preprocesses pathological parameter information in the data set by:
respectively calculating the average value and the standard deviation of different characteristic data in the data set; screening abnormal data, namely screening abnormal points by using a 3 delta principle, concentrating 99.7 percent of data in each dimension characteristic in an interval range of [ mu-3 delta, mu +3 delta ] according to a formula Pr (mu-3 delta is not more than X and not more than mu +3 delta) and approximately equal to 0.9973, wherein the abnormal data points are all characteristic data points outside the range of characteristic data values; wherein X represents an observed value of a normal distribution, μ represents an average value of the distribution, and δ represents a standard deviation of the normal distribution; processing abnormal data, namely replacing abnormal points and missing values of discrete data by a median method, and processing the abnormal points and the missing values in continuous data by an average method; and (3) standardizing the data, namely converting the features with different magnitudes in the data set into scores with unified measurement by adopting a Z-score standardization mode, and improving the comparability between each feature.
Preferably, in step S3, the training process of the BP neural network of the prediction model prediction module is: the system firstly initializes the network by the network layer number of the BP neural network, the number of the neurons contained in each layer and three parameters of an activation function, and initializes the weight and the threshold of each neuron; and marking whether the preprocessed data set has the risk of diseases or not, carrying out one-hot coding on the mark, taking the mark as input, taking the value of the loss function as a guide, adjusting the weight parameter and the bias parameter of each neuron until network training is finished, and outputting the trained neural network parameter set.
Preferably, in step S3, when inputting pathological parameter data of the elderly, a parameter is set for each newly added elderly user, the parameter is used to record the number of times that the elderly user registers an effective cardiovascular disease pathological parameter in one month, when the parameter is higher than a preset threshold, the pathological parameter information of the elderly user is input into the trained BP neural network to determine the risk of cardiovascular disease, and when there is a risk of cardiovascular disease, the system alarms through the client and clears the parameter.
Preferably, in step S1, the data set includes fourteen features extracted from the open source data set, which are age, gender, chest pain type, resting blood pressure, cholesterol, fasting blood glucose, resting electrocardiogram result, maximum heart rate value, whether or not exercise induced angina, change of slope of exercise induced ST wave, slope of ST wave during peak period of exercise, number of blood vessels stained in fluoroscopy, whether or not thalassemia is present, and diagnosis type of cardiovascular disease.
The invention has the beneficial effects that:
the cardiovascular disease prediction system needs to carry out a large amount of data operation to realize the prediction work of cardiovascular diseases, so a certain requirement is required on the aspect of system data processing speed, and compared with a Hadoop big data frame, the Spark big data frame based on the elastic distributed data set of the memory does not need to store intermediate results in a hard disk during data calculation, so the calculation speed is higher, and the Spark big data platform can not increase too much economic cost along with the reduction of the price of a memory bank of a computer. The cardiovascular disease pathological parameter information of middle-aged and old people in the community is stored and managed based on the big data platform, the collection cost of pathological parameter collection of middle-aged and old people in the community is reduced, and meanwhile, the utilization rate of the pathological parameter information is also improved. The prediction model prediction module acquires first data through the data acquisition module, and then the risk of cardiovascular diseases can be calculated through a formula according to the acquired first data; adopt machine learning algorithm to judge the sick risk of middle-aged and old people's cardiovascular disease, guarantee that the old person that can be better is healthy, reduced community doctor's work burden simultaneously.
Detailed Description
The present invention will be described in detail with reference to specific embodiments, which are illustrative of the present invention and are not to be construed as limiting the present invention.
A method for predicting cardiovascular data of middle-aged and old people comprises the following steps:
s1, constructing a data management module of cardiovascular disease pathological parameter information based on Spark SQL components, and uniformly managing, storing and inquiring the pathological parameter information of middle-aged and elderly people received from multiple community hospital clients by using the data management module;
step S2, inputting first data through a data management module, wherein the first data comprises a single selection of sex, a condition of taking antihypertensive drugs, a condition of smoking and a condition of diabetes; inputting specific numerical values in input boxes corresponding to the age and the systolic pressure; identifying the age data and the systolic pressure data after natural logarithm conversion processing by adopting an identification unit; the condition data of taking the antihypertensive drug includes that the taken antihypertensive drug is identified as 1, and the condition data of not taking the antihypertensive drug is identified as 0; identifying smoking as 1 and identifying non-smoking as 0 in the smoking condition data; the diabetes condition data has a diabetes identification of 1 and no diabetes identification of 0;
step S3, in a prediction model prediction module, training a BP neural network by using a preprocessed data set, and judging the cardiovascular disease risk of the middle-aged and the elderly by combining pathological parameter information input by a client based on the trained BP neural network;
and step S4, presenting the prediction result to a user through the result output module.
It is to be noted that the collection of pathological parameters of cardiovascular diseases. The cardiovascular disease prediction system collects data of pathological parameters of cardiovascular diseases for the elderly through a plurality of community hospitals. Moreover, the community hospital needs to submit the pathological parameter information of each elderly user to the Spark big data platform through the client.
And (5) a human-computer interaction interface. The system achieves the purpose of information interaction with the user through the interactive interface of the client. The user can change the password information of the own account through the client and can also apply for the cardiovascular disease through the client interactive interface. And the client presents the prediction result information of the system background to the user in a popup mode to realize the aim of man-machine interaction.
And storing the pathological parameter information of the cardiovascular diseases of the middle-aged and the elderly. The HIVE data warehouse comprises metadata, the system stores and uniformly manages the pathological parameter information of the users received from the community hospital clients in a HIVEON Spark mode, and platform support is provided for later analysis of the pathological parameter information of the community old people.
Hadoop has been widely applied in various fields as the earliest mainstream platform of big data ecosphere, but Hadoop has the disadvantages of low operation efficiency, uncomfortable flow calculation and the like. Spark is a big data frame improved on the basis of Hadoop, and compared with the two big data frames, the Hadoop big data frame is more prone to the storage function of big data, and the speed is relatively low when a calculation task is executed; the Spark big data framework is more suitable for application scenarios requiring a large amount of data calculation, and the calculation speed is relatively fast. The prediction system of the invention needs a large amount of data operation to realize the prediction work of cardiovascular diseases, so a certain requirement is made on the aspect of system data processing speed. In addition, with the development of DDR memory calculation, the price of a memory bank of a computer is greatly reduced, so that the economic cost is not increased too much when a Spark big data platform is built. In conclusion, the Spark big data platform is more suitable for the construction work of cardiovascular disease prediction systems of middle-aged and elderly people.
Specifically, in this embodiment, in step S1, the client is used to realize the interaction between the system and the user, the user registers the pathological parameter information of the elderly through the client, and the system presents the risk probability of cardiovascular disease of the elderly and the health advice to the user through the client.
Specifically, in this embodiment, in step S1, a data preprocessing module is used to preprocess the pathological parameter information in the data set for training the BP neural network.
Specifically, in this embodiment, in step S1, based on the Spark SQL component, the data management module includes a HIVE data warehouse, a Spark calculation engine, and an HDFS storage unit, and is configured to perform data query, storage, and unified management on the pathological parameter information received from multiple community hospital clients.
Specifically, in this embodiment, the step of preprocessing the pathological parameter information in the data set by the data preprocessing module is as follows:
respectively calculating the average value and the standard deviation of different characteristic data in the data set; screening abnormal data, namely screening abnormal points by using a 3 delta principle, concentrating 99.7 percent of data in each dimension characteristic in an interval range of [ mu-3 delta, mu +3 delta ] according to a formula Pr (mu-3 delta is not more than X and not more than mu +3 delta) and approximately equal to 0.9973, wherein the abnormal data points are all characteristic data points outside the range of characteristic data values; wherein X represents an observed value of a normal distribution, μ represents an average value of the distribution, and δ represents a standard deviation of the normal distribution; processing abnormal data, namely replacing abnormal points and missing values of discrete data by a median method, and processing the abnormal points and the missing values in continuous data by an average method; and (3) standardizing the data, namely converting the features with different magnitudes in the data set into scores with unified measurement by adopting a Z-score standardization mode, and improving the comparability between each feature.
It should be noted that almost all data will be within 3 standard deviations of the mean. Therefore, data outside the range of 3 standard deviations of the mean of the data can be considered as outlier data. Based on such criteria, outliers of the data set can be selected. Since the data property is considered, the embodiment only processes the data outliers of the five features of age, bps, chl, hr, oldpeak. By calculating the average value mu and the standard deviation delta of the five characteristics in the data set, abnormal points contained in the five characteristics can be accurately screened out. In addition, for attributes like gender, the value of the data set is only selected from 0 and 1, and for the outlier, the value of the data set is calibrated to be-1, so that the outlier of the data can be easily screened out.
And (4) processing abnormal data. Through the steps, abnormal values and missing values in the data set can be determined, wherein the abnormal values and the missing values comprise discrete data and continuous data. The method adopts a median method to replace abnormal points and missing values of discrete data, and aims to process the abnormal points and the missing values of continuous data by adopting an averaging method so as not to destroy the value characteristics of the discrete data
Specifically, in this embodiment, in the step S3, the training process of the BP neural network of the prediction model prediction module is as follows: the system firstly initializes the network by the network layer number of the BP neural network, the number of the neurons contained in each layer and three parameters of an activation function, and initializes the weight and the threshold of each neuron; and marking whether the preprocessed data set has the risk of diseases or not, carrying out one-hot coding on the mark, taking the mark as input, taking the value of the loss function as a guide, adjusting the weight parameter and the bias parameter of each neuron until network training is finished, and outputting the trained neural network parameter set.
Specifically, in this embodiment, in step S3, when inputting pathological parameter data of middle-aged and elderly people, a parameter is set for each newly added elderly user, the parameter is used to record the number information of registration of pathological parameters of effective cardiovascular diseases performed by the elderly user within one month, when the parameter is higher than a preset threshold, the pathological parameter information of the elderly user is input into the trained BP neural network to determine the risk of cardiovascular diseases, and when there is a risk of cardiovascular diseases, the system alarms and clears the parameter through the client.
Specifically, in this embodiment, in step S1, the data set includes fourteen features extracted from the open source data set, which are the age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood glucose, resting electrocardiogram result, maximum heart rate value, whether or not angina is induced by exercise, change in slope of ST wave caused by exercise, slope of ST wave during peak period of exercise, number of blood vessels stained in fluoroscopy, whether or not thalassemia is present, and diagnosis type of cardiovascular disease.
The cardiovascular disease prediction system needs to carry out a large amount of data operation to realize the prediction work of cardiovascular diseases, so a certain requirement is required on the aspect of system data processing speed, and compared with a Hadoop big data frame, the Spark big data frame based on the elastic distributed data set of the memory does not need to store intermediate results in a hard disk during data calculation, so the calculation speed is higher, and the Spark big data platform can not increase too much economic cost along with the reduction of the price of a memory bank of a computer. The cardiovascular disease pathological parameter information of middle-aged and old people in the community is stored and managed based on the big data platform, the collection cost of pathological parameter collection of middle-aged and old people in the community is reduced, and meanwhile, the utilization rate of the pathological parameter information is also improved. The prediction model prediction module acquires first data through the data acquisition module, and then the risk of cardiovascular diseases can be calculated through a formula according to the acquired first data; adopt machine learning algorithm to judge the sick risk of middle-aged and old people's cardiovascular disease, guarantee that the old person that can be better is healthy, reduced community doctor's work burden simultaneously.
The technical solutions provided by the embodiments of the present invention are described in detail above, and the principles and embodiments of the present invention are explained herein by using specific examples, and the descriptions of the embodiments are only used to help understanding the principles of the embodiments of the present invention; meanwhile, for a person skilled in the art, according to the embodiments of the present invention, there may be variations in the specific implementation manners and application ranges, and in summary, the content of the present description should not be construed as a limitation to the present invention.
Claims (8)
1. A method for predicting cardiovascular data of middle-aged and old people is characterized by comprising the following steps:
s1, constructing a data management module of cardiovascular disease pathological parameter information based on Spark SQL components, and uniformly managing, storing and inquiring the pathological parameter information of middle-aged and elderly people received from multiple community hospital clients by using the data management module;
step S2, inputting first data through a data management module, wherein the first data comprises a single selection of sex, a condition of taking antihypertensive drugs, a condition of smoking and a condition of diabetes; inputting specific numerical values in input boxes corresponding to the age and the systolic pressure; identifying the age data and the systolic pressure data after natural logarithm conversion processing by adopting an identification unit; the condition data of taking the antihypertensive drug includes that the taken antihypertensive drug is identified as 1, and the condition data of not taking the antihypertensive drug is identified as 0; identifying smoking as 1 and identifying non-smoking as 0 in the smoking condition data; the diabetes condition data has a diabetes identification of 1 and no diabetes identification of 0;
step S3, in a prediction model prediction module, training a BP neural network by using a preprocessed data set, and judging the cardiovascular disease risk of the middle-aged and the elderly by combining pathological parameter information input by a client based on the trained BP neural network;
and step S4, presenting the prediction result to the user through the result output module.
2. The method for predicting cardiovascular data of middle-aged and elderly people according to claim 1, wherein the method comprises:
in the step S1, the interaction between the system and the user is realized through the client, the user registers the pathological parameter information of the elderly through the client, and the system presents the risk probability of cardiovascular diseases of the elderly and health advice to the user through the client.
3. The method for predicting cardiovascular data of middle-aged and elderly people according to claim 1, wherein the method comprises: in step S1, a data preprocessing module is used to preprocess the pathological parameter information in the data set for training the BP neural network.
4. The method for predicting cardiovascular data of middle-aged and elderly people according to claim 1, wherein the method comprises: in step S1, based on the Spark SQL component, the data management module includes a HIVE data warehouse, a Spark calculation engine, and an HDFS storage unit, and is configured to perform data query, storage, and unified management on the pathological parameter information received from multiple community hospital clients.
5. The method for predicting cardiovascular data of middle-aged and elderly people according to claim 3, wherein the method comprises: the data preprocessing module preprocesses pathological parameter information in the data set by the following steps:
respectively calculating the average value and the standard deviation of different characteristic data in the data set; screening abnormal data, namely screening abnormal points by using a 3 delta principle, concentrating 99.7 percent of data in each dimension characteristic in an interval range of [ mu-3 delta, mu +3 delta ] according to a formula Pr (mu-3 delta is not more than X and not more than mu +3 delta) and approximately equal to 0.9973, wherein the abnormal data points are all characteristic data points outside the range of characteristic data values; wherein X represents an observed value of a normal distribution, μ represents an average value of the distribution, and δ represents a standard deviation of the normal distribution; processing abnormal data, namely replacing abnormal points and missing values of discrete data by a median method, and processing the abnormal points and the missing values in continuous data by an average method; and (3) standardizing the data, namely converting the features with different magnitudes in the data set into scores with unified measurement by adopting a Z-score standardization mode, and improving the comparability between each feature.
6. The method for predicting cardiovascular data of middle-aged and elderly people according to claim 1, wherein the method comprises: in step S3, the training process of the BP neural network of the prediction model prediction module is as follows: the system firstly initializes the network by the network layer number of the BP neural network, the number of the neurons contained in each layer and three parameters of an activation function, and initializes the weight and the threshold of each neuron; and marking whether the preprocessed data set has the risk of diseases or not, carrying out one-hot coding on the mark, taking the mark as input, taking the value of the loss function as a guide, adjusting the weight parameter and the bias parameter of each neuron until network training is finished, and outputting the trained neural network parameter set.
7. The method for predicting cardiovascular data of middle-aged and elderly people according to claim 1, wherein the method comprises: in step S3, when inputting pathological parameter data of the elderly, a parameter is set for each newly added elderly user, the parameter is used to record the number of times that the elderly user registers an effective cardiovascular disease pathological parameter in one month, when the parameter is higher than a preset threshold, the pathological parameter information of the elderly user is input into the trained BP neural network to determine the risk of cardiovascular disease, and when there is a risk of cardiovascular disease, the system alarms through the client and clears the parameter.
8. The method for predicting cardiovascular data of middle-aged and elderly people according to claim 1, wherein the method comprises: in step S1, the data set includes fourteen features extracted from the open source data set, which are age, gender, chest pain type, resting blood pressure, cholesterol, fasting blood glucose, resting electrocardiogram result, maximum heart rate value, whether or not angina is induced by exercise, change of slope of ST wave caused by exercise, slope of ST wave during peak exercise period, number of blood vessels stained in fluoroscopy, whether or not thalassemia is present, and diagnosis type of cardiovascular disease.
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