Research On Cardiovascular Disease Prediction System Based On Machine Learning

According to data from chronic disease surveys in China,the number of patients diagnosed with chronic diseases in the country has reached 260 million.China is the country with the largest number of chronic diseases in the world.Chronic diseases are a kind of high morbidity,high mortality,and can not be cured for a lifetime disease,so that chronic disease has become the biggest obstacle to the development of China's medical and health undertakings.Due to the particularity of chronic diseases,the study found that disease prevention is the most effective measure in the management of chronic diseases.Therefore,research on disease prediction is of great significance for improving the efficiency of chronic disease management.With the development of the Internet and big data,the form and quantity of medical data are increasing.People begin to use mathematical models in disease research.Through quantitative analysis methods to study the characteristics and principles of the disease.Because machine learning method can get better accuracy when,dealing with complex data problems,more and more people are used to predict the disease.In this context,this paper aims to establish a chronic disease prediction model by using machine learning methods.Based on this,a chronic disease risk prediction system is established.Through this system,the risk of the user's chronic diseases can be predicted and the early warning and disease of high-risk groups can be realized.Intervention to achieve effective management of chronic diseases.The main research content of this article is as follows:(1)Proposed a new type of chronic disease management.Through the study of the current traditional chronic disease management model in China,the existing problems in the traditional chronic disease management model are analyzed,and a new type of chronic disease management model is proposed in combination with the new generation of information technology,emphasizing the importance of disease prediction in chronic disease management;(2)The prediction model of chronic disease was constructed.In order to implement the disease prediction in the new chronic disease management model,the model is established by the integrated method of supervised learning in machine learning.Finally,the model is simulated.The results show that the prediction efficiency of the stochastic forest model is higher than the logical regression and the decision tree,and the use of the model is better than that of the logic regression and decision tree.The model can predict the chronic diseases relatively accurately.(3)Developed a chronic disease prediction system.The object-oriented analysis method is used to analyze and design the system,and the main function modules of the system are implemented in Java language.This article studies the machine learning-based chronic disease prediction system,predicts and intervenes before the occurrence of chronic diseases,and reduces the incidence of chronic diseases from the source.The study of this system has great reference value for the management of chronic diseases.