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All Journal JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science KLIK: Kajian Ilmiah Informatika dan Komputer
Dwi Rosa Indah
Universitas Sriwijaya, Palembang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : building of informatics technology and science

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Comparative Evaluation of Machine Learning Models with Explainability and Fairness for Diabetes Risk Prediction Vanya Dwi Nabila; Bayu Wijaya Putra; M Rudi Sanjaya; Dwi Rosa Indah
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.10763

Abstract

Diabetes mellitus remains a major global health challenge, making early risk prediction essential for timely intervention and prevention. This study proposes a trustworthy machine learning framework for diabetes risk prediction by integrating predictive performance, explainability, and fairness evaluation. Six classification algorithms Logistic Regression, Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost were comparatively evaluated, with LightGBM selected as the best baseline model. Hyperparameter optimization was subsequently performed using Optuna with macro F1-score as the optimization objective to better address the imbalanced multiclass nature of the dataset. Model performance was assessed using Accuracy, Balanced Accuracy, Precision, Recall, F1-score, and Receiver Operating Characteristic–Area Under the Curve (ROC-AUC). Model explainability was analyzed using SHapley Additive exPlanations (SHAP), while fairness was evaluated using Fairlearn based on gender and race through Demographic Parity Difference and Equalized Odds Difference.Experimental results show that hyperparameter optimization increased Balanced Accuracy from 0.3755 to 0.4891 and macro F1-score from 0.3797 to 0.4333, indicating improved recognition of minority classes. Although overall Accuracy decreased from 0.8357 to 0.7023, this trade-off reflects a more balanced classification across diabetes categories, which is preferable for imbalanced clinical datasets where identifying minority cases is essential for early risk detection. SHAP analysis identified Body Mass Index (BMI), Age, Physical Health Days, Mental Health Days, and Income Level as the most influential predictors. Fairness evaluation further demonstrated low demographic disparities across gender and race. These findings demonstrate that integrating predictive performance, explainability, and fairness enables the development of a more transparent, equitable, and clinically reliable diabetes risk prediction framework.
Komparasi Random Forest dan XGBoost Berbasis SMOTE dalam Penanganan Ketidakseimbangan Data untuk Prediksi Risiko Stroke Chairunnisa Desti Arzety; Bayu Wijaya Putra; M Rudi Sanjaya; Dwi Rosa Indah
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.10740

Abstract

Class imbalance is one of the main obstacles in building machine learning models for stroke prediction, as this condition tends to make models learn the characteristics of the majority class more dominantly than those of the minority class. This study aims to examine the comparison and evaluate the performance between the Random Forest and XGBoost algorithms in predicting stroke risk, by applying SMOTE as a data balancing technique. The study uses the Kaggle Stroke Prediction Dataset, consisting of 5,110 patient records. The research stages include data cleaning, categorical feature representation using TF-IDF, numerical feature normalization through Standard Scaling, data splitting using the Stratified Train-Test Split technique, application of SMOTE on the training data, and model development using the Random Forest and XGBoost algorithms. Model performance was evaluated using several metrics, namely Accuracy, Precision, Recall, F1-Score, ROC-AUC, Confusion Matrix, and Feature Importance. The test results show that XGBoost achieved a Recall of 0.1600, an F1-Score of 0.1702, and an ROC-AUC of 0.7877—all higher than those of Random Forest, which achieved a Recall of 0.0800, an F1-Score of 0.1111, and an ROC-AUC of 0.7068. Although these results represent an improvement over Random Forest, the still relatively low Recall and F1-Score values indicate that the model’s ability to detect stroke cases is not yet optimal and therefore requires further development. Furthermore, SMOTE was applied to balance the class distribution in the training data so that the model’s learning process would give greater attention to the minority class. This study contributes by implementing a consistent preprocessing pipeline, including data cleaning, Standard Scaling, TF-IDF-based categorical feature representation, Stratified Train-Test Split, and SMOTE on the training data, enabling a more objective comparison of Random Forest and XGBoost through the use of comprehensive evaluation metrics on an imbalanced stroke prediction dataset.
Co-Authors A Salman Alfarizi Adelia Faradilla Bayu Wijaya Putra Chairunnisa Desti Arzety Dea Salsa Bila Hardini Novianti Iwan Mandala Putra M Riyandra Alfariz Silaen M Rudi Sanjaya Mgs Afriyan Firdaus Putri Eka Sevtiyuni Vanya Dwi Nabila