Prediction of Heart Disease Using Machine Learning with Data Mining

Authors: Tanyildizi Kökkülünk H*

DOI: 10.23880/psbj-16000228

Volume 7, Issue 1

Abstract

Aim: In this study, it was aimed to make a categorical estimation of the absent/presence of heart disease by using some parameters (age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thalach) of healthy and heart disease individuals. Material and Methods: The classification was obtained with multiple linear regression (MLR) of machine learning in the R Studio program. Machine learning has been improved by selecting parameters that have a high contribution to the prediction by using the Akaike information criterion.

Keywords: Heart; AIC criterion; Machine learning; Prediction

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