cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Triwiyanto
Contact Email
triwiyanto123@gmail.com
Phone
+628155126883
Journal Mail Official
triwiyanto123@gmail.com
Editorial Address
Pucang Jajar Timur No. 10, Surabaya, Indonesia
Location
Kota surabaya,
Jawa timur
INDONESIA
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568624     DOI : https://doi.org/10.35882/ijeeemi
Core Subject : Health, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics (IJEEEMI) publishes peer-reviewed, original research and review articles in an open-access format. Accepted articles span the full extent of the Electronics, Biomedical, and Medical Informatics. IJEEEMI seeks to be the world’s premier open-access outlet for academic research. As such, unlike traditional journals, IJEEEMI does not limit content due to page budgets or thematic significance. Rather, IJEEEMI evaluates the scientific and research methods of each article for validity and accepts articles solely on the basis of the research. Likewise, by not restricting papers to a narrow discipline, IJEEEMI facilitates the discovery of the connections between papers, whether within or between disciplines. The scope of the IJEEEMI, covers: Electronics: Intelligent Systems, Neural Networks, Machine Learning, Fuzzy Systems, Digital Signal Processing, Image Processing, Electromedical: Biomedical Signal Processing and Control, Artificial intelligence in biomedical imaging, Machine learning and Pattern Recognition in a biomedical signal, Medical Diagnostic Instrumentation, Laboratorium Instrumentation, Medical Calibrator Design. Medical Informatics: Intelligent Biomedical Informatics, Computer-aided medical decision support systems using heuristic, Educational computer-based programs pertaining to medical informatics
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 231 Documents
 20   21   22   23   24 
An Explainable XGBoost Framework for Mortality Prediction in People Living with HIV Receiving Antiretroviral Therapy Ni Putu Likayuni Viona; Ngakan Nyoman Kutha Krisnawijaya; Desak Nyoman Widyanthini
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 3 (2026): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i3.383

Abstract

Mortality remains an important concern among people living with HIV (PLHIV) receiving antiretroviral therapy (ART), particularly when high-risk patients are not identified early. This study presents an explainable mortality prediction approach based on an optimized Extreme Gradient Boosting (XGBoost) classifier. A retrospective dataset containing 393 clinical records was analyzed. Before model training, duplicate entries were removed, missing values were imputed, categorical variables were encoded, and class imbalance in the training data was addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Optuna was employed to determine suitable XGBoost hyperparameters, while SHapley Additive exPlanations (SHAP) were used to examine how individual predictors affected the model output. Performance was evaluated on an independent testing set using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC-AUC). The resulting classifier achieved an accuracy of 89.87%, weighted precision of 0.91, weighted recall of 0.90, weighted F1-score of 0.90, and an ROC-AUC of 0.948. For the mortality class, recall reached 0.97, with 30 of 31 mortality cases correctly detected. Follow-up duration and functional status contributed most strongly to the predictions. SHAP analysis further showed whether these predictors shifted individual outputs toward mortality or survival. The integration of optimized predictive modeling with explainable artificial intelligence enabled accurate mortality prediction while providing transparent insight into how individual predictors influenced the model output. The findings suggest that the model may assist in identifying patients who require closer monitoring, although validation using external and multicenter data is necessary before clinical implementation.

Page 24 of 24 | Total Record : 231

 20   21   22   23   24 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol. 8 No. 3 (2026): August Vol. 8 No. 2 (2026): May Vol. 8 No. 1 (2026): February Vol. 7 No. 4 (2025): November Vol. 7 No. 3 (2025): August Vol. 7 No. 2 (2025): May Vol. 7 No. 1 (2025): February Vol. 6 No. 4 (2024): November Vol. 6 No. 3 (2024): August Vol. 6 No. 2 (2024): May Vol. 6 No. 1 (2024): February Vol. 5 No. 4 (2023): November Vol. 5 No. 3 (2023): August Vol. 5 No. 2 (2023): May Vol. 5 No. 1 (2023): February Vol. 4 No. 4 (2022): November Vol. 4 No. 3 (2022): August Vol. 4 No. 2 (2022): May Vol. 4 No. 1 (2022): February Vol. 3 No. 4 (2021): November Vol. 3 No. 3 (2021): August Vol. 3 No. 2 (2021): May Vol. 3 No. 1 (2021): February Vol. 2 No. 3 (2020): November Vol. 2 No. 2 (2020): August Vol. 2 No. 1 (2020): February Vol. 1 No. 2 (2019): November Vol. 1 No. 1 (2019): August More Issue