Comparing Results of Classification Techniques Regarding Heart Disease Diagnosing

被引：0

作者：

AL Badr, Benan Abdullah ^{[1
]}

AL Ghezzi, Raghad Suliman ^{[1
]}

AL Moqhem, ALjohara Suliman ^{[1
]}

Eljack, Sarah ^{[1
]}

机构：

[1] Majmaah Univ, Dept Comp Sci & Informat, Coll Sci, Az Zulfi 11952, Saudi Arabia

来源：

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY | 2022年 / 22卷 / 05期

关键词：

Google Colab; classification technique; Random Forest; !text type='Python']Python[!/text] language; machine learning;

D O I：

10.22937/IJCSNS.2022.22.5.20

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Despite global medical advancements, many patients are misdiagnosed, and more people are dying as a result. We must now develop techniques that provide the most accurate diagnosis of heart disease based on recorded data. To help immediate and accurate diagnose of heart disease, several data mining methods are accustomed to anticipating the disease. A large amount of clinical information offered data mining strategies to uncover the hidden pattern. This paper presents, comparison between different classification techniques, we applied on the same dataset to see what is the best. In the end, we found that the Random Forest algorithm had the best results.

引用

页码：135 / 142

页数：8

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