Abstract
Abstract: Cardiovascular Disease (CVD) currently stands as the leading cause of death worldwide. Clinical data analytics encounter a significant challenge in accurately predicting cardiac disease. The healthcare industry generates vast volumes of raw data, necessitating its transformation into meaningful insights through machine learning techniques. The objective is to leverage machine learning models to improve the predictability of survival among cardiac patients. This study employs machine learning classifiers: Random Forest, Gradient Boosting classifier, Extra Tree Classifier, XG-Boost, Ada Boost and Hybrid models. The Synthetic Minority Oversampling Technique (SMOTE) addresses the challenge posed by imbalanced datasets. Experimental results indicate that employing the SMOTE technique enhances the accuracy of the chosen classifier's predictions. Among these classifiers, Hybrid Model stands out with the highest accuracy of 89.82% when applied to predicting the survival of cardiac illness afterimplementing SMOTE
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