Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal PkM MIFTEK Journal of Intelligent Systems Technology and Informatics
Fitriyani, Dila
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Intelligent Systems Technology and Informatics

 1 
Classification of Thyroid Disease Risk Using the XGBoost Method Amelia, Melina; Fitriyani, Dila
Journal of Intelligent Systems Technology and Informatics Vol 1 No 3 (2025): JISTICS, November 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i3.26

Abstract

Thyroid disease is one of the essential health threats and requires early detection to enable more effective medical intervention. This study aims to develop a classification model using the XGBoost algorithm to categorize patient clinical data from the Kaggle platform into three levels of thyroid cancer risk: low, moderate, and high. The data processing process follows the stages of the SEMMA (Sample, Explore, Modify, Model, Assess) methodology, with main techniques such as label coding, stratified 5-fold cross-validation, and hyperparameter tuning being applied. Performance evaluation was conducted using accuracy metrics, including F1-score and AUC-ROC. The results show that the model exhibits excellent performance in detecting low-risk cases (AUC = 1.00), but it still faces challenges in classifying moderate and high-risk categories. After adjusting the hyperparameters, the validation accuracy increased to 96.24%, although the final accuracy on the test data remained at 69.85%. These findings suggest that XGBoost is a promising approach for the early assessment of thyroid disease risk, particularly in detecting low-risk cases. However, further model development is needed to enhance generalizability across risk levels and support informed clinical decision-making.
Co-Authors Adawiyah, Alya Amelia, Melina Ardhillah, Zian Zaky Baasith, Azry Abdul Dini Destiani Siti Fatimah Diniyaturobiah, Hanipah Febrian, Rivan Husaeni, Fachri Ahmad Al Khaerurijal, Fajar Maridjan, Maula Muhammad Maulana, Rifki Ilham Muhtadin, Fauzan Azmi Mujahid, Wildan Mulyana, Abdurrahman Mustofa, Muhamad Zaenal Muzaky, Rifky Khoerul Noviansyah, Ikhwan R, Alvarizky Putra Kurniawan Rafiqi, Putri Aufa Resita, Rasty Saputra, Muhamad Dzaki