Enhancing Heart Disease Prediction through Ensemble Learning Techniques with Hyperparameter Optimization

Heart disease is a significant global health issue, contributing to high morbidity and mortality rates. Early accurate heart prediction crucial for effectively preventing managing the condition. However, this remains challenging task achieve. This study proposes machine learning model that leverages various preprocessing steps, hyperparameter optimization techniques, ensemble algorithms predict disease. To evaluate performance of our model, we merged three datasets from Kaggle have similar features, creating comprehensive dataset analysis. By employing extra tree classifier, normalizing data, utilizing grid search cross-validation (CV) optimization, splitting with an 80:20 ratio training testing, proposed approach achieved impressive accuracy 98.15%. These findings demonstrated potential accurately predicting presence or absence Such predictions could significantly aid in early prevention, detection, treatment, ultimately reducing associated