Heart Disease Prediction Using Machine Learning

Baban. U. Rindhe, N. Ahire, Rupali Patil, Shweta Gagare, Manisha Darade
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引用次数: 2

Abstract

Heart disease is one of the most common and serious health issues in all the age groups. The food habits, mental stress, smoking, etc. are a few reasons for heart diseases. Diagnosing heart issues at an early stage is very much important to take proper treatment. The treatment of heart disease at the later stage is very expensive and risky. In this chapter, the authors discuss machine learning approaches to predict heart disease from a set of health parameters collected from a person. The heart disease dataset from the UCI machine learning repository is used for the study. This chapter discusses the heart disease prediction capability of four well-known machine learning approaches: naive Bayes classifier, KNN classifier, decision tree classifier, random forest classifier.
利用机器学习预测心脏病
心脏病是所有年龄组中最常见和最严重的健康问题之一。饮食习惯、精神压力、吸烟等是导致心脏病的几个原因。早期诊断心脏问题对于采取适当的治疗非常重要。心脏病晚期的治疗非常昂贵且有风险。在本章中,作者讨论了从一组从人身上收集的健康参数中预测心脏病的机器学习方法。该研究使用了UCI机器学习存储库中的心脏病数据集。本章讨论了四种著名的机器学习方法的心脏病预测能力:朴素贝叶斯分类器、KNN分类器、决策树分类器、随机森林分类器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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