Key result
Random Forest with SMOTE predicts cardiac illness survival with ~95% accuracy.
Why the study?
Clinical data analytics face challenges in predicting cardiac disease due to unbalanced datasets and the need for improved machine learning model accuracy.
Applying SMOTE to balance datasets improves the accuracy of machine learning models, particularly Random Forest, in predicting cardiac disease survival.
May aid cardiac prediction model development; hypothesis-generating and requires prospective validation before clinical use.
Cardiovascular Disease (CVD) is presently the biggest reason of death globally. Clinical data analytics face a huge hurdle when attempting to predict cardiac disease. Massive amounts of raw data generated by the healthcare business are transformed into meaningful insights using machine learning techniques. The goal is to use of machine learning models that can enhance the predictability of cardiac patient survival. This paper employs eight machine learning classifiers: Decision Tree (DT), Extra Tree (ET), Random Forest (RF), Adaptive Boosting (AdaBoost), Ridge Classifier (RC), Linear Discriminant Analysis (LDA) and Light Gradient Boosting Machine (Light GBM) for prediction of cardiac disease. Synthetic Minority Oversampling Technique (SMOTE) is used to resolve the issue of unbalance dataset. Experiment outcomes demonstrate that SMOTE technique improves the accuracy of the selected classifier's output and Random Forest achieves highest accuracy with 95.12% applying SMOTE in predicting the survival of cardiac illness.
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Sudipta Priyadarshinee and Madhumita Panda (2022) studied Cardiovascular Disease. Random Forest with Synthetic Minority Oversampling Technique (SMOTE) vs. Other machine learning classifiers was evaluated on Accuracy in predicting the survival of cardiac illness. Random Forest combined with Synthetic Minority Oversampling Technique (SMOTE) achieved the highest accuracy of 95.12% in predicting the survival of cardiac illness.
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