Handling Imbalanced Heart Disease Data and Explaining the Factors - Das, Sandip; Sajjan, Gairik; Dasgupta, Tamojit; Poddar, Arkajyoti; Patty, Sayani; Ghosh, Debmitra (International Journal of Computer Sciences and Engineering, 2026)
Heart disease is one of the most serious and life threatening problems. If predicted beforehand, many lives can be saved. But, the problem is that medical datasets are highly imbalanced, which leads machine learning algorithms to perform poorly on the minority class. Which in terms leads to wrong predictions. In healthcare it is highly risky to predict something wrongly, because, people’s lives are on stake. The ratio of minority and majority class data should be 1:1, or near about equal, in order to get a good result. Synthetic Minority Oversampling TEchnique(SMOTE) is one such oversampling technique that makes it come true, which is used in this work. In addition we have used eXplainable AI(XAI) to better visualise the predictions. We have used LIME (Local Interpretable Model-agnostic Explanation) and SHAP (Shapely Additive Explanations) algorithms to understand the contributions of features towards the predictions.