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All Journal JISKa (Jurnal Informatika Sunan Kalijaga) Letters in Information Technology Education (LITE)
Kristian, Tadem Vergi
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Computer Science & IT Education Electrical & Electronics Engineering Library & Information Science

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Enhancing Medical Education with Machine Learning: A Case Study on CKD Detection Using AdaBoost-SVM Erizal, Erizal; Suwarto, Suwarto; Basuki, Umar; Hiswati, Marselina Endah; Diqi, Mohammad; Kristian, Tadem Vergi
Letters in Information Technology Education (LITE) Vol 7, No 2 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um010v7i22024p56-62

Abstract

This study explores the application of the AdaBoost-SVM model for the classification of chronic kidney disease (CKD), addressing the critical need for accurate and early diagnosis in clinical settings. Using a dataset of 400 instances with 25 clinical features, we implemented rigorous data cleaning to remove rows with missing values, ensuring high-quality input data. The AdaBoost-SVM model achieved remarkable performance, with an overall accuracy of 96%. Precision and recall were notably high for both 'ckd' and 'notckd' classes, reflecting the model’s robustness and reliability. These results underscore the potential of hybrid machine learning approaches in medical diagnostics, providing valuable insights into improving CKD detection. Although the study has several limitations, such as a limited dataset and the exclusion of incomplete data, its findings clearly show the model's usefulness and provide a foundation for future research. Future work should focus on larger, more diverse datasets and alternative data handling techniques to enhance the model's applicability and performance. This research highlights the promise of integrating advanced algorithms into clinical decision-making processes, ultimately aiming to improve patient outcomes through early and accurate disease detection.
Enhancing Diabetes Classification Using a Relaxed Online Maximum Margin Algorithm Meliala, Dyan Avando; Sulistyawati, Arum Kurnia; Diqi, Mohammad; Hiswati, Marselina Endah; Kristian, Tadem Vergi
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 3 (2025): September 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.3.267-278

Abstract

Diabetes mellitus is a growing global health concern that requires accurate and reliable classification models for early diagnosis and effective management. Traditional machine learning models often struggle with class imbalance, generalization limitations, and high false-positive rates, leading to misdiagnoses and delayed interventions. This study enhances the Relaxed Online Maximum Margin Algorithm (ROMMA) to improve the accuracy of diabetes classification. Using a publicly available dataset from Kaggle, which contains 768 medical records with nine health attributes, the model’s performance was evaluated through a confusion matrix and classification metrics. The Enhanced ROMMA achieved an accuracy of 92%, significantly improving upon the Standard ROMMA’s 85% accuracy. The recall for diabetes detection increased from 0.83 to 0.94, reducing false negatives and ensuring more accurate patient identification. While slight misclassification still exists, this improvement enhances the model’s reliability for clinical applications. Future research should incorporate larger datasets and advanced techniques to enhance robustness and generalizability. This study contributes to the development of more accurate machine learning models for diabetes prediction, ultimately supporting better healthcare decision-making.
Co-Authors Arum Kurnia Sulistyawati Basuki, Umar Erizal Erizal Marselina Endah Hiswati Meliala, Dyan Avando Mohammad Diqi Suwarto Suwarto