심장마비 예측을 위한 머신러닝 알고리즘의 성능 비교 및 주요 변수 분석 = A Comparative Study on the Performance of Machine Learning Algorithms and Key Feature Analysis for Predicting Heart Attack

다국어 초록 (Multilingual Abstract)

In this study, we compare the performance of various machine learning algorithms for predicting heart attacks, a major cause of mortality globally, with a focus on identifying key predictive features. Using a dataset of 918 records, the research evaluates models such as Random Forest, Logistic Regression, XGBoost, SVM, KNN, and Decision Tree to enhance prediction accuracy for heart attack risks. The methodology emphasizes robust preprocessing techniques, including feature scaling and handling class imbalances through Stratified K-Fold cross-validation, to improve model reliability. Results reveal that ensemble models, particularly Random Forest, achieve the highest ROC AUC score of 0.9301, significantly outperforming traditional algorithms. Key predictors, such as ST_Slope, were identified as critical variables in determining heart attack risks, while less influential features, such as RestingECG, had minimal impact. The findings underscore the efficacy of ensemble learning in predicting heart attacks and highlight the importance of feature importance analysis in enhancing model interpretability. This study provides valuable insights into the integration of machine learning in personalized healthcare, offering a foundation for future research to refine predictive models and improve early detection and prevention strategies for cardiovascular diseases.