Predictive Linear Regression Model for Premature Birth Risk Assessment System

Abstract

Preterm birth is a major cause of neonatal mortality in Indonesia and is influenced by multiple maternal factors. Early prediction models are crucial for supporting timely clinical decision-making and reducing adverse maternal–infant outcomes. The method of this study developed a linear regression–based predictive model using 915 pregnancy medical records from Dr. H. M. Ansari Saleh Regional Hospital, Banjarmasin (2020–2022). The workflow included data preprocessing, feature selection, Min-Max normalization, and experimentation with various train–test split ratios (90:10 to 50:50). Model performance was evaluated using R², Adjusted R², MAE, MSE, RMSE, and MAPE metrics. As the results, the 70:30 split ratio achieved the best accuracy of 89.05% and AUC of 98.10%, with low prediction errors. Optimizations with Adamax and Nadam enhanced stability and reduced MAPE to 1.95%. The optimized linear regression model reliably predicts preterm birth risk and is suitable for clinical decision support, particularly in resource-limited settings.