摘要
应用机器学习技术预测疾病是现代医学的研究热点之一。为采用数据驱动方法预测心脏病风险,本文分别应用K-均值聚类、层次聚类、高斯混合模型、逻辑回归、随机森林和神经网络来预测心脏病风险。结果显示,聚类算法能够揭示不同心脏病类型之间的潜在联系,分类算法能够提供心脏病的辅助诊断。
The application of machine learning technology to predict diseases is one of the research hotspots in modern medicine.To use data-driven methods to predict heart disease risk,this paper applies K-means clustering,hierarchical clustering,Gaussian mixture model,logistic regression,random forest,and neural network to predict heart disease risk.The results show that clustering algorithms can reveal potential connections between different types of heart disease,and classification algorithms can provide auxiliary diagnosis for heart disease.
作者
杨敬桑
罗胤
YANG Jingsang;LUO Yin(Department of Electronics and Information Engineering,Liuzhou Vocational&Technical College,Liuzhou,China,545006)
出处
《福建电脑》
2024年第8期12-16,共5页
Journal of Fujian Computer
关键词
心脏病
聚类算法
分类算法
预测模型
Heart Disease
Clustering Algorithm
Classification Algorithm
Prediction Model
