Development and Evaluation of Stroke Disease Classification Models: Classical Machine Learning, Deep Learning, and Explainable AI Approaches

Kusuma, Lianny Wydiastuty and Wijaya, Andri and Sihotang, Asahiro Nathanael Star and Giap, Yo Ceng (2025) Development and Evaluation of Stroke Disease Classification Models: Classical Machine Learning, Deep Learning, and Explainable AI Approaches. JUITA: Jurnal Informatika, 13 (3). pp. 383-394. ISSN 2579-8901

[img] Text (Article)
Development and Evaluation of Stroke Disease Classification Models.pdf - Published Version
Download (281kB)
[img] Text (Turnitin)
Turnitin - Development and Evaluation of Stroke Disease Classification Models.pdf - Published Version
Download (986kB)
[img] Text
Korespondensi Jurnal.pdf
Download (1MB)

Abstract

This study evaluates the impact of the Synthetic Minority Oversampling Technique (SMOTE) on improving machine learning and deep learning performance in stroke risk classification using secondary, publicly available data from Kaggle’s Stroke Prediction Dataset (n = 5,110; 249 stroke cases, 4,861 non-stroke cases), for deep learning. Performance was measured using accuracy, precision, recall, and F1-score, while Explainable AI (XAI) methods (SHAP, LIME) were utilized for interpretability. The results show that applying SMOTE improves the model's sensitivity to the minority "Stroke" class, with Random Forest after SMOTE achieving 97% accuracy and a balanced precision–recall. These findings highlight the methodological potential of combining SMOTE with machine learning, deep learning, and XAI; however, they should not be interpreted as direct clinical validation. Future work with clinical and population-based datasets is necessary to assess the applicability in real-world healthcare settings.

Item Type: Article
Uncontrolled Keywords: deep learning, machine learning, SMOTE, stroke, XAI
Subjects: 000 Karya Umum > 006 Metode Komputer Tertentu > 006.242 Kode Bar > 006.3 Kecerdasan Buatan
000 Karya Umum > 006 Metode Komputer Tertentu > 006.3 Kecerdasan Buatan
Divisions: Fakultas Sains & Teknologi > Teknik Informatika
Depositing User: Hariyanto Rie
Date Deposited: 31 Aug 2026 07:37
Last Modified: 31 Aug 2026 07:37
URI: https://repositori.buddhidharma.ac.id//id/eprint/3504

Actions (login required)

View Item View Item