Advanced Feature Engineering and Sampling Technique for Effective Heart Disease Prediction System using Machine Learning Technique

Predicting heart disease is crucial in healthcare, where accurate detection can significantly impact patient outcomes. A major challenge in prediction is imbalanced data, with underrepresented samples in the minority class leading to biased models. This study addresses this by combining advanced feature engineering with SMOTE (Synthetic Minority Oversampling Technique) to manage class imbalance. The objectives include refining data through feature engineering, building predictive models using machine learning algorithms, and assessing performance metrics. Algorithms such as Random Forest, SVM, Decision Tree, Logistic Regression, and K Nearest Neighbor are applied. Results show that robust feature engineering notably enhances performance, with Random Forest achieving the highest accuracy (0.9604), followed by SVM (0.954) and Decision Tree (0.947). The findings highlight the role of feature engineering and sampling in improving heart disease prediction models, with meaningful implications for healthcare advancements.