支持向量机
机器学习
疾病
计算机科学
人工智能
随机森林
依赖关系(UML)
人口
逻辑回归
数据挖掘
医学
病理
环境卫生
作者
Weicheng Sun,Ping Zhang,Zilin Wang,Dongxu Li
出处
期刊:ASP transactions on Internet of things
[Advancing Science Press Limited]
日期:2021-05-29
卷期号:1 (1): 30-35
被引量:29
标识
DOI:10.52810/tiot.2021.100035
摘要
With the rapid development of artificial intelligence, it is very important to find the pattern of the data from the observed data and the functional dependency relationship between the data. By finding the existing functional dependencies, we can classify and predict them. At present, cardiovascular disease has become a major disease harmful to human health. As a disease with high mortality, the prediction problem of cardiovascular disease is becoming more and more urgent. However, some computer methods are mainly used for disease detection rather than prediction. If the computer method can be used to predict cardiovascular disease in advance and treat it as early as possible, then the consequences of the disease can be reduced to a certain extent. Diseases can be predicted by mechanical methods. Support vector machine (SVM) has strict mathematical theory support, and can deal with nonlinear classification after using kernel techniques. Therefore, support vector machine can be used to predict cardiovascular disease. On the other hand, we also use logical regression and random forest to predict cardiovascular disease. This paper mainly uses the method of machine learning to predict whether the population is sick or not. First of all, we preprocess the obtained data to improve the quality of the data, and then use svm and logical regression to predict, so as to provide reference for the prevention and treatment of cardiovascular diseases.
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