The usefulness of machine learning models in forecasting the risk of a heart attack based on health-related variables is examined in this study. The classification models Gaussian Naive Bayes, K-Nearest Neighbors and Random Forest were created and assessed using performance measures like recall, accuracy, precision, F1-score. The dataset was heavily preprocessed, handling null values, duplicates, outliers, and feature transformation. It had 10 predictor variables and a target variable with 5110 observations. The most instructive elements for model training were found using feature selection approaches. Using k-fold cross-validation for KNN and GridSearchCV for Random Forest, hyperparameter tweaking was carried out for the models on the remaining 25% of the dataset after they had been trained on 75% of it. The results show that KNN outperformed Gaussian Naive Bayes and Random Forest, with the greatest accuracy of 96.4% following hyperparameter adjustment. SMOTE was also used to improve model robustness by addressing class imbalance. In summary, this study's best model for predicting the likelihood of a heart attack was KNN. These results demonstrate how machine learning models can improve early detection and individualized patient care by advancing risk assessment and intervention tactics in the healthcare industry.

Heart Attack Prediction Machine Learning Models K-Nearest Neighbors (KNN) Gaussian Naive Bayes Random Forest Data Preprocessing SMOTE Hyperparameter Tuning Healthcare Data Risk Prediction.