Article Per Year (5 Year)
p-Index From 2021 - 2026
1.006
P-Index
This Author published in this journals
All Journal Journal of Computer Science and Research Journal Of Artificial Intelligence And Software Engineering
Khaidar, Al
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Computer Science and Research

 1 
Classification of Health Indicators for Diabetes Mellitus Prediction Using a TabTransformer Model on Clinical Tabular Data Khaidar, Al; Kurnia, Sri
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 2 (2024): April: Computer Science
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v2i2.54

Abstract

Diabetes mellitus is a non-communicable disease with a continuously increasing global prevalence and impacts quality of life and long-term economic burden; therefore, data-driven early detection is crucial to prevent clinical complications. This study aims to develop a diabetes prediction model using the TabTransformer architecture by utilizing a clinical dataset from Kaggle containing 100,000 patient profiles with more than 35 relevant numerical and categorical attributes. The research stages include preprocessing to remove potential leakage features, target and feature separation, numerical normalization, and categorical feature embedding. The TabTransformer model is applied for binary classification (diagnosed_diabetes) by utilizing a self-attention mechanism to capture latent interactions between tabular features, and is evaluated using accuracy, precision, recall, F1-score, and ROC AUC metrics. The results show competitive performance with an accuracy of 82.55%, a diabetes class F1-score of 0.8527, and a ROC AUC value of 0.9009, indicating the model's discriminatory ability to reliably distinguish diabetic and non-diabetic patients. Based on these results, the TabTransformer architecture has been proven effective for processing large-scale clinical tabular data and is worthy of consideration in the implementation of an artificial intelligence-based medical decision support system for predicting chronic diseases, especially diabetes mellitus.
Co-Authors Alqhifari, Azka Arnawan Hasibuan Azzanna, Maghriza Hermansyah Hermansyah Jessika, Jessika Kurnia, Sri M. Ali, Rahmadi Muhammad Daud Muliana, Syarifah Nurdin Nurdin Rahmad Rahmad Rusadi, Athirah