High-risk pregnancy prediction using Taguchi-optimized machine learning methods and TOPSIS-based model selection

High-risk pregnancies pose a major challenge in maternal and neonatal healthcare, defined as conditions where medical, social, or obstetric factors threaten the health of the mother or fetus. Traditional clinical tools often overlook individual complexities and delay identification, especially in resource-limited settings where mortality rates remain high. This study addresses these gaps by developing machine learning (ML) models for high-risk pregnancy prediction using data from 62 pregnant women at an Iranian hospital (2014-2016). Features were categorized into demographics, pregnancy-related, and complete case (all features). Preprocessing, feature selection, and hyperparameters were optimized using the Taguchi method. Five supervised models, K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained and evaluated using multiple performance and statistical criteria. The study contributes an integrated framework combining feature-group comparison, Taguchi-based optimization, and TOPSIS-based multi-criteria model ranking to support balanced model selection. Using real-world hospital-based data from Iran, it jointly predicts major high-risk pregnancy conditions, including IUFD, IUGR, and preterm birth. Results showed pregnancy-related features as stronger predictors, with KNN achieving 88% accuracy and high recall in this category. TOPSIS ranked SVM highest for demographics, KNN for pregnancy-related, and MLP for the complete case. This framework may support clinicians and maternal health centers in earlier identification and prioritization of high-risk pregnancies.