Machine Learning Based Heart Disease Prediction Using Random Forest

Abstract

A recent study by the World Health Organization sheds light on the alarming increase in cardiovascular diseases, contributing to approximately 17.9 million deaths annually. This study delves into the effectiveness of employing the Random Forest algorithm, a robust machine learning approach, to forecast the likelihood of heart disease based on diverse risk factors. By leveraging a dataset encompassing demographic, clinical, and lifestyle attributes, the Random Forest model underwent training to categorize individuals into two groups: those with or without heart disease. Through meticulous feature selection and ensemble learning, the algorithm adeptly captures intricate relationships among predictors, thereby augmenting prediction accuracy. Evaluation metrics including accuracy and AUC-ROC curve were employed in order to determine model's effectiveness. Impressively, our model achieves a prediction accuracy of 97%. Moreover, a comparative analysis with other prominent machine learning models such as Naive Bayes, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Decision Tree revealed that the Random Forest approach outperforms others in terms of accuracy and efficiency in prediction tasks. Keywords: Random Forest (RF), Machine Learning (ML), Accuracy, Classification.