Open AccessBagging Technique to Reduce Misclassification in Coronary Heart Disease Prediction Based on Random Forest
Author(s) -
Aries Saifudin,
U. U. Nabillah,
. Yulianti,
Teti Desyani
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1477/3/032009
Subject(s) - random forest , coronary heart disease , computer science , heart disease , machine learning , framingham risk score , coronary disease , disease , artificial intelligence , medicine , cardiology
Knowing the existence of coronary heart disease is very important to reduce the risk caused. Coronary heart disease is influenced by many factors, in diagnose requires complex analysis. Many proposed the application of a machine-learning algorithm to diagnose/predict coronary heart disease, but have not given perfect results (excellent). The machine learning algorithm is used to classify someone affected by coronary heart disease or not based on factors that have been determined input. The results of diagnosis/prediction are not perfect due to misclassification that is still large.to reduce misclassification, bagging techniques are proposed. The classification algorithm used in the study is Random Forest. Experimental results show that bagging techniques can reduce misclassified predictions of coronary heart disease.