Search Results for B. Shunmugapriya

Abstract

Predicting the risk of operative delivery, especially Cesarean section, is important for improving outcomes for both mothers and newborns. In this study, a machine-learning-based framework is developed using explainable artificial intelligence techniques across three clinical datasets: fetal cardiotocography, maternal health parameters, and pregnancy outcome data.Three classification models—Logistic Regression, Decision Tree, and Random Forest—were evaluated to identify the most effective approach. Among these, the Random Forest model achieved the best performance, with an F1 Score of 0.88 and an Area Under the Curve of 0.99 on the fetal health dataset.To enhance interpretability in a clinical context, a dual-layer explanation strategy was adopted. SHapley Additive exPlanations was used to analyze global feature importance, while Local Interpretable Model-agnostic Explanations was applied to explain individual predictions. The results indicate that abnormal short-term variability is a key factor influencing operative delivery risk. The explanations generated by both methods were consistent with established clinical understanding, making the model both accurate and interpretable.