A Predictive Model for Cardiovascular Diseases Using Data Mining Techniques

Cardiometry

article

Avneesh Kumar¹  Santosh Kumar Singh²  Shruti Sinha³ 

[1] Department of Computer Applications, Galgotias University Greater Noida;School of Health & Allied Science, ARKA JAIN University Jharkhand;School of Allied Science, Dev Bhoomi Uttarakhand University Dehradun

关键词: Cardiovascular Disease (CVD);    Cardiomyopathy;    Data mining;    Decision Tree;    Neural Network;   

DOI  :  10.18137/cardiometry.2022.24.367372

学科分类：环境科学（综合）

来源: Russian New University

【 摘 要 】

In many countries, heart disease is the main cause of mortality. Heart diseases are often identified by doctors based on recent clinical trial results and their prior experience treating patients who present comparable symptoms. Patients with heart disease require early diagnosis, prompt treatment or constant monitoring. The purpose of this study is to look into the numerous data mining technologies that have recently been developed for predicting heart disease. According to observations, 15 feature neural networks outperform all other data mining methodologies. Another finding from the analysis is that decision trees using genetic algorithms or feature subset selection have good accuracy. The results illustrate that the same classifier can occasionally produce precise results that change depending on the data mining approach used. According to the findings, a neural network with 15 characteristics has so far achieved a maximum efficiency of 100%. Decision Tree, on the other hand, has also done well, with 98.65% accuracy and 15 features.

【 授权许可】

CC BY