Open AccessA Comprehensive Performance Analysis of Various Classifier Models for Coronary Artery Disease Prediction
Publication year - 2021
Publication title -
international journal of cognitive informatics and natural intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.164
H-Index - 24
eISSN - 1557-3966
pISSN - 1557-3958
DOI - 10.4018/ijcini.20211001oa23
Subject(s) - naive bayes classifier , decision tree , computer science , support vector machine , machine learning , artificial intelligence , logistic regression , boosting (machine learning) , classifier (uml) , random forest , predictive modelling , bayes' theorem , bayesian probability
Cardio Vascular Diseases (CVD) is the major reason for the death of the majority of the people in the world. Earlier diagnosis of disease will reduce the mortality rate. Machine learning (ML) algorithms are giving promising results in the disease diagnosis and it is now widely accepted by medical experts as their clinical decision support system. In this work, the most popular ML models are investigated and compared with one other for heart disease prediction based on various metrics. The base classifiers such as Support Vector Machine (SVM), Logistic regression, Naïve Bayes, Decision Tree, K Nearest Neighbour are used for predicting heart disease. In this paper, bagging and boosting techniques are applied over these individual classifiers to improve the performance of the system. With the Cleveland and Statlog datasets, Naive Bayes as the individual classifier gives the maximum accuracy of 85.13%and 84.81% respectively. Bagging technique improves the accuracy of the decision tree which is identified as a weak classifier by 7% and it is a significant improvement in identifying CVD.