Ensemble Learning untuk Klasifikasi Penyakit Kardiovaskuler dengan Optimasi Hyperparameter menggunakan Grid Search - Eko Nur Wahyudi; Setyawan Wibisono (Universitas Stikubank, 2026)
This study aims to optimize ensemble learning models for cardiovascular disease classification based on patients’ clinical data using the Grid Search method. The dataset used consists of 70,000 patient records with clinical attributes such as age, gender, height, weight, blood pressure, cholesterol, glucose, smoking habits, alcohol consumption, physical activity, and cardiovascular disease status. After preprocessing, 68,606 records were utilized, with a relatively balanced class distribution. The algorithms employed in this study include LightGBM, AdaBoost, and Gradient Boosting. Model evaluation was conducted using accuracy, precision, recall, F1-score, ROC-AUC, confusion matrix, and 10-fold cross validation. The results indicate that Gradient Boosting achieved the best performance with a ROC-AUC score of 0.804400 on the testing data and 0.801757 on cross validation. This model also produced the highest recall and F1-score values. Therefore, Gradient Boosting optimized with Grid Search is considered suitable for cardiovascular disease classification based on clinical data.