An Effective Heart Disease Prediction Framework based on Ensemble Techniques in Machine Learning

Abstract

To design a framework for effective prediction of heart disease based on ensemble techniques, without the need of feature selection, incorporating data balancing, outlier detection and removal techniques, with results that are still at par with cutting-edge research. In this study, the Cleveland dataset, which has 303 occurrences, is used from the UCI repository. The dataset comprises 76 raw attributes, however only 14 of them are listed by the UCI repository as significant risk factors for heart disease when the dataset is uploaded as an open source dataset. Data balancing strategies, such as random over sampling, are used to address the issue of unbalanced data. Additionally, an isolation forest is used to find outliers in multivariate data, which has not been explored in previous research. After eliminating anomalies from the data, ensemble techniques such as bagging, boosting, voting, stacking are employed to create the prediction model. The potential of the proposed model is assessed for accuracy, sensitivity, and specificity, positive prediction value (PPV), negative prediction value (NPV), F1 score, ROC-AUC and model training time. For the Cleveland dataset, the performance of the suggested methodology is superior, with 98.73% accuracy, 98% sensitivity, 100% specificity, 100% PPV, 97% NPV, 1 as F score, and AUC as 1 with comparatively very less training time. The results of this study demonstrate that our proposed approach significantly outperforms the existing scholarly work in terms of accuracy and all the stated performance metrics. No earlier research has focused on these many performance parameters.