Cardiovascular disease risk prediction network model based on multiple parameters and construction method thereof

Abstract

The invention discloses a cardiovascular disease risk prediction network model based on multiple parameters and a construction method thereof, relates to a risk prediction model, and solves the problems that an existing cardiovascular disease risk prediction model cannot predict multiple physiological parameters and is not ideal in prediction effect. The method comprises the following steps: establishing a cardiovascular disease data set; preprocessing the data set data, and dividing the data set into a training set and a test set according to the ratio of the number of the training set data to the number of the test set data being 7: 3; performing model construction: both the training set and the test set comprise samples and labels, model training is conducted on training set data through the minimum error of forward propagation and reverse propagation in the training process, and the trained model is evaluated through the test set data. The risk of suffering from cardiovascular diseases is evaluated by detecting multiple physiological parameters such as age, gender, chest pain type, resting blood pressure, serum cholesterol, fasting blood glucose, resting electrocardiogram, maximum heart rate and the like of a person.

Description

technical field [0001] The invention relates to a risk prediction model, in particular to a multi-parameter-based cardiovascular disease risk prediction network model and a construction method thereof. Background technique [0002] Accurate prediction of cardiovascular disease risk is of great significance for the prevention and early treatment of cardiovascular disease. According to the 2018 China Cardiovascular Disease Report, about 290 million people in China suffer from cardiovascular disease, and the mortality rate is as high as 40% of the resident mortality rate. Among them, the mortality rate of cardiovascular disease in rural areas continues to be higher than that in urban areas. The prevalence and mortality of cardiovascular diseases are still on the rise in my country. How to reduce and avoid cardiovascular disease is the focus of work to reduce the mortality rate of cardiovascular disease, and accurate examination of people at risk of cardiovascular disease is the...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem that the existing cardiovascular disease risk prediction model cannot predict various physiological parameters and the prediction effect is not ideal, the present invention provides a multi-parameter-based cardiovascular disease risk prediction network model and its construction method

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