An Analytical Study of Improved Machine Learning Approaches for Predicting Mode of Delivery

Machine learning approaches came about as a game-changer in modern healthcare, leading to more reliable medical predictions and enhanced patient care. Predicting the way of delivery during labor is critical to protecting mother and newborn health. This study offers a thorough comparison of machine learning (ML) approaches aiming at predicting an optimal mode of delivery. The efficacy of enhanced ML algorithms is evaluated in improving prediction accuracy using a dataset containing maternal health indicators. This study compares the effectiveness of five distinct machine learning approaches: J48, Logistic Model Trees, Random Forests, Random Tree, and Multilayer Perceptron. We analyze their prediction capabilities and applicability for the task at hand through a thorough experimental procedure. Our findings show that different advanced ML techniques have varying degrees’ effectiveness in forecasting the way of delivery. The performance parameters under consideration are accuracy, precision, and recall. Among all the performance metrics considered, J48 exhibited the most favorable performance.