Boosting the Accuracy of Cardiovascular Disease Prediction Through SMOTE

Abstract

Cardiovascular Disease (CVD) affects deaths and hospitalisations. Clinical data analytics struggles to predict heart disease survival. This report compares machine learning-based cardiovascular disease prediction studies. The authors use a Kaggle dataset of 70,000 records and 16 features to show a SMOTE model with hyperparameter-optimized classifiers. Random Forest outperforms KNN with 13 elements in cardiovascular disease prediction. Naive Bayes outperforms SVM on complete feature sets. The proposed model achieves 86% accuracy, and the optimised SMOTE technique outperforms the traditional SMOTE technique in all metrics. This study analyses the strengths and weaknesses of existing models for making cardiovascular disease predictions with machine learning and suggests a promising new method.

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