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All Journal METIK JURNAL International Journal of Engineering, Science and Information Technology
Zakaria Ahmad Dahlan
RSUD Inche Abdoel Moeis
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Ensemble Learning untuk Model Prediksi Risiko Preeklamsia dan Explainable AI Berbasis SHAP Yudhi Fajar Saputra; Milkhatun; Mahmoud Ahmad Al-Khasawneh; Yazeed Al Moaiad; Aldi Bastiatul Fawait; Sitti Rahmah; Zakaria Ahmad Dahlan
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/kp377403

Abstract

Preeclampsia is a pregnancy complication that poses significant risks to both mother and fetus. Early prediction of preeclampsia risk is crucial to improve maternal healthcare outcomes. This study aims to develop a predictive model for preeclampsia risk using ensemble learning approaches and to enhance model interpretability through Explainable Artificial Intelligence (XAI). The dataset consists of 332 pregnant women who received antenatal care, with 330 complete clinical records after data cleaning. Two ensemble learning algorithms, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), were implemented and evaluated using Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), and additional classification metrics. The best-performing model was further analyzed using SHapley Additive exPlanations (SHAP) to assess feature contributions at both global and individual levels. The results indicate that XGBoost outperformed Random Forest with an AUC of 0.81 compared to 0.72 after applying class weighting and 5-fold cross-validation. XGBoost also demonstrated more balanced performance with an accuracy of 0.83, recall of 0.85, and specificity of 0.60. In contrast, Random Forest achieved an accuracy of 0.91 and specificity of 0.98 but failed to detect positive cases, with a recall of 0.00, indicating bias toward the majority class. SHAP analysis reveals that height, weight, age at menarche, and the number of antenatal care (ANC) visits significantly influence prediction, while hypertension consistently contributes to increased risk. This study demonstrates that integrating ensemble learning with XAI improves both predictive performance and model transparency for preeclampsia risk assessment.
Performance Analysis of Ensemble Learning Models Comparing Bagging and Boosting Techniques for Early Preeclampsia Risk Detection in Pregnant Women Prediction Yudhi Saputra; Milkhatun Milkhatun; Aldi Bastiatul Fawait; Zakaria Ahmad Dahlan; Yazeed Al Moaiad; Haviluddin Haviluddin; Rayner Alfred
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1880

Abstract

Preeclampsia is a major pregnancy complication that substantially contributes to maternal morbidity and mortality worldwide, making early identification of risk factors essential for effective prevention and timely clinical intervention. This study evaluates the performance of ensemble learning models by comparing bagging and boosting techniques to develop an accurate early prediction system for preeclampsia risk using clinical medical record data. The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, encompassing data understanding, data preparation, modeling, evaluation, and interpretation. The dataset was obtained from RSUD Inche Abdoel Moeis Samarinda and underwent preprocessing procedures, including data cleaning, transformation, feature encoding, normalization, and Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Six ensemble learning algorithms were evaluated, consisting of Random Forest, Extra Trees, and Rotation Forest as bagging methods, and XGBoost, LightGBM, and CatBoost as boosting methods. Model performance was assessed using accuracy, precision, recall, and weighted F1-score. The experimental results demonstrate that Random Forest achieved the highest predictive performance, with an accuracy of 0.92, precision of 0.93, recall of 0.92, and weighted F1-score of 0.91, indicating superior robustness and generalization capability. Extra Trees achieved comparable accuracy (0.92) but exhibited lower prediction stability across evaluation metrics. Among the boosting algorithms, LightGBM and CatBoost each obtained an accuracy of 0.89, while XGBoost achieved 0.88. Rotation Forest recorded the lowest accuracy (0.62), suggesting limited suitability for this clinical dataset. These findings indicate that bagging-based ensemble methods, particularly Random Forest, outperform boosting techniques for imbalanced clinical data and provide strong empirical support for developing reliable Clinical Decision Support Systems (CDSS) for early preeclampsia screening and risk assessment in healthcare settings
Co-Authors Aldi Bastiatul Fawait Aldi Bastiatul Fawait Haviluddin Haviluddin Mahmoud Ahmad Al-Khasawneh Milkhatun Milkhatun, Milkhatun Rayner Alfred Sitti Rahmah Yazeed Al Moaiad Yazeed Al Moaiad Yudhi Fajar Saputra Yudhi Saputra