An Explainable AI Driven Machine Learning Approach for Maternal Health Risk Analysis

Abstract

Maternal health during pregnancy is a severe issue, particularly in the rural areas of developing countries like Bangladesh, where a lack of access to healthcare and inadequate infrastructure increase risks. Maternal healthcare has a great deal of difficulty due to the absence of reliable tools for forecasting health concerns. Negative results frequently result from the traditional method's inability to diagnose and manage pregnancy- related problems correctly. While there are several ways to monitor maternal health conditions, machine learning has the potential to increase diagnosis accuracy, efficiency, and speed. In this research, by using several machine learning classifiers, we built a model that can analyze the maternal health risk during pregnancy. The Maternal Health Risk dataset from the UCI machine learning repository was used in this study. SMOTE was utilized to address the class imbalance data and generated an additional 99000 data. We assess the model before and after using SMOTE. Accuracy, precision, recall, and F1-Score were utilized to evaluate the model's performance. Extreme Gradient Boosting (XGBoost) is our standout performer, with an accuracy of 84% and 95% before and after using SMOTE, respectively. Additionally, Explainable AI was used to increase the model's readability. This study demonstrates the power of machine learning, which could revolutionize maternal health care by identifying maternal health risks early.

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