Parameter Optimization Based Mud Ring Algorithm for Improving the Maternal Health Risk Prediction

Abstract

Maternal health risk prediction is a critical aspect of public health. This paper proposes a new parameter optimization method to improve maternal health risk prediction using the Mud Ring Algorithm (MRA). The proposed method is implemented in two stages to achieve the desired improvement. In the first stage, the MRA is used to optimize the parameter of Support Vector Machine (SVM) classifier. The MRA-optimized SVM (MRA-SVM) is then evaluated on thirteen real-world datasets to compare its performance with other state-of-the-art optimization algorithms. Subsequently, the performance of the MRA-SVM is specifically compared using a maternal health risk dataset. The maternal health risk dataset, being a medical dataset, faces a significant issue of imbalance. To address this problem, the second stage comes in turn, and the crossover oversampling technique is first employed. Furthermore, other classifiers such as Random Forest and K-Nearest Neighbor are also used for improving the prediction of the maternal health risk dataset. Experimental results indicate that our proposed method delivers highly competitive results compared to six well-known optimization algorithms, evaluated based on Accuracy, G-mean, F-Measure, MCC and Kappa metrics. Moreover, the results demonstrate that using a crossover oversampling method as a preprocessing step, along with MRA to optimize the parameters of SVM (MRA-SVM), Random Forest (MRA-RF), and K-Nearest Neighbor (MRA-KNN), increases prediction accuracy of maternal health risk dataset by 11.8%, 9.11%, and 17.08% respectively, compared to using the three classifiers without MRA and crossover oversampling. In sum, utilizing the crossover oversampling method, combined with the MRA to optimize the parameter of Random Forest classifier, leads to higher prediction performance compared to other recently published algorithms.