Heart Disease Diagnosis using a Machine Learning Algorithm

被引：0

作者：

Chandrasegar, T. ^{[1
]}

Choudhary, Aman ^{[1
]}

机构：

[1] VIT, SITE, Vellore, Tamil Nadu, India

来源：

2019 INNOVATIONS IN POWER AND ADVANCED COMPUTING TECHNOLOGIES (I-PACT) | 2019年

关键词：

Heart disease diagnosis; hyper-parameter tuning; Machine learning; Random forest; Support vector machine; Decision tree; DECISION-SUPPORT-SYSTEM; PREDICTION;

D O I：

10.1109/i-pact44901.2019.8959989

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Machine Learning turns the extensive collection of raw healthcare data into information that can help to make informed decision and prediction. This research named fine-tune prediction (FTP) model aims to identify significant features and incorporate hybrid classifier to improve accuracy. For this, the Cleveland heart samples taken from the UCI repository. Experiment results show that the proposed FTP model achieves 93.49% accuracy. Without FTP, the RF, and LM achieve only 88.20% and 63.60% accuracy. We also present the hyper-parameter tuning of the classifier with significant features in the result section.

引用

页数：4

共 29 条

[1] Decision making model to predict presence of coronary artery disease using neural network and C5.0 decision tree [J].