Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization

In the medical domain, early identification of cardiovascular issues poses a significant challenge. This study enhances heart disease prediction accuracy using machine learning techniques. Six algorithms (random forest, K-nearest neighbor, logistic regression, Naïve Bayes, gradient boosting, and AdaBoost classifier) are utilized, with datasets from Cleveland IEEE Dataport. Optimizing model accuracy, GridsearchCV, five-fold cross-validation employed. dataset, regression surpassed others 90.16% while excelled in Dataport achieving 90% accuracy. A soft voting ensemble classifier combining all six further enhanced resulting 93.44% for dataset 95% dataset. performance classifiers on both datasets. study’s novelty lies use GridSearchCV hyperparameter optimization, determining best parameters model, assessing negative log loss metrics. also examined each fold to evaluate model’s benchmark The approach improved accuracies and, when compared existing studies, this method notably exceeded their results.