A Knowledge Based Framework for Cardiovascular Disease Prediction

Abstract

Cardiovascular disease has become more concern in the hectic and stressful life of modern era. Machine learning techniques are becoming reliable in medical treatment to help the doctors. But the ML algorithms are sensitive to data sets. Hence a Smart Robust Predictive System is almost essential which can work efficiently on all data sets. The study proposes ensembled classifier validating its performance on five different data sets-Cleveland, Hungarian, Long Beach, Statlog and Combined datasets. The developed model deals with missing values and outliers. Synthetic Minority Oversampling Technique (SMOTE) was used to resolve the class imbalance issue. In this study, performance of five individual classifiers - Support Vector Machine Radial (SVM-R), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF) and XGBoost, was compared with five ensembled classifiers on five different data sets. On each data set the top three performers were identified and were combined to give ensemble classifiers. Thus, in all total 25 experimentation were done. The results have shown that out of all classifiers implemented, the proposed system outperforms on all the data sets. The performance was validated by 10-fold cross validation The proposed system gives the highest accuracy and sensitivity of 87% and 86% respectively.