AN AI-BASED MATERNAL HEALTH RISK PREDICTION MODEL USING MULTILAYER PERCEPTRON AND COMPREHENSIVE DATA PREPROCESSING - Lutfi, Dien Kartika (Mitra Husada Health Internasional Conference (MIHHICo), 2026)
Background: Maternal health risk detection is essential for reducing pregnancy-related complications and improving maternal and fetal outcomes. However, conventional risk assessment remains challenging due to data quality issues and limitations in accurately identifying high-risk pregnancies. Artificial Intelligence (AI) offers promising solutions for improving maternal health risk prediction. Objectives: This study aims to develop and evaluate an AI-based maternal health risk prediction model using the Multilayer Perceptron (MLP) classifier and to assess the contribution of comprehensive data preprocessing techniques in improving prediction performance. Methods: The study utilized the Maternal Health and High-Risk Pregnancy Dataset containing maternal clinical and physiological indicators. Data preprocessing included missing value imputation, duplicate removal, outlier treatment using the capping method, feature scaling with Robust Scaler, and class balancing through Random Oversampling (ROS). The MLP classifier was trained using a supervised learning approach and evaluated using Accuracy, Precision, Recall, F1-score, Specificity, and Classification Error. Results: The proposed model achieved an accuracy of 97.60%, precision of 97.07%, recall of 98.20%, F1-score of 97.62%, specificity of 96.99%, and a classification error of 2.40%, demonstrating excellent predictive performance. Conclusions: The integration of comprehensive preprocessing techniques with the MLP classifier significantly improves maternal health risk prediction. The proposed model shows strong potential to support early risk detection, clinical decision-making, and preventive maternal healthcare through accurate and reliable AI-based prediction.