Penerapan dan Perbandingan Algoritma SVM, Naive Bayes, dan Gradient Boosting dalam Prediksi Stroke - Joseph Melchior Nababan; Iqro Mukti Arto; Putra Satria; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan (Lembaga Pengembangan Kinerja Dosen, 2025)
Stroke is a major cardiovascular disease that significantly contributes to global mortality and disability rates. Early detection through stroke risk prediction is essential in reducing its impact. This study focuses on evaluating and comparing the performance of three machine learning algorithms—Support Vector Machine (SVM), Naive Bayes (NB), and Gradient Boosting (GB)—in predicting stroke occurrence. The research utilizes the Healthcare Stroke Dataset, which contains 5,109 records and 11 predictor variables. Modeling was performed using Orange Data Mining software, with 70% of the data allocated for training and 30% for testing. The results show that the SVM algorithm achieved the highest performance, obtaining an AUC score of 0.919 and an accuracy of 96.0%, followed by Gradient Boosting with an AUC of 0.885 and accuracy of 95.2%, and Naive Bayes with an AUC of 0.803 and accuracy of 88.2%. Therefore, SVM is identified as the most effective algorithm for predicting stroke risk within this dataset.