The Impactness of SMOTE as Imbalance Class Handling for Myocardial Infarction Complication Classification using Machine Learning Approach with Data Imputation and Hyperparameter - Ahmad Tajali; Saragih, Triando Hamonangan; Mazdadi, Muhammad Itqan; Budiman, Irwan; Farmadi, Andi (Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia, 2024)
Myocardial Infarction (MI) is a critical medical emergency characterized by the sudden blockage of blood flow to the heart muscle, often resulting from a blood clot in a coronary artery that has been narrowed by atherosclerotic plaque buildup. This condition demands immediate attention, as prolonged disruption of blood supply can cause irreversible damage to the heart muscle. Diagnosing MI typically involves a combination of methods, including a physical examination, electrocardiogram (ECG) analysis, blood tests to measure heart-specific enzymes, and imaging techniques such as coronary angiography. Early prediction of potential MI complications is crucial to prevent severe outcomes and improve patient prognosis. This study focuses on the early prediction of MI complications through the application of machine learning classification methods. We employed algorithms such as Support Vector Machine (SVM), Random Forest, and XGBoost to analyze patient medical records and accurately predict these complications. The selection of Support Vector Machine (SVM), Random Forest, and XGBoost in this study is driven by their proven effectiveness in handling complex classification problems. To manage incomplete datasets and preserve valuable information, data imputation techniques like K-Nearest Neighbors (KNN) Imputation, Iterative Imputation, and MissForest were applied. KNN, Iterative, and MissForest imputations were chosen to handle missing data due to their effectiveness in preserving data integrity, which is crucial for accurate predictions in myocardial infarction complication studies. Additionally, Bayesian Optimization was utilized to fine-tune the hyperparameters of the models, thereby enhancing their predictive accuracy. The Iterative Imputation method yielded the best performance, particularly in SVM and XGBoost algorithms. SVM achieved 100% accuracy, precision, sensitivity, F1 score, and Area Under the Curve (AUC), while XGBoost attained 99.4% accuracy, 100% precision, 79.6% sensitivity, an F1 score of 88.7%, and an AUC of 0.898. While XGBoost and MissForest proved to be the most successful pairing, the overall effectiveness of the models suggests that Iterative Imputation and Random Forest also have potential under certain conditions.