Enhancing Maternal Outcome Prediction Using Explainable Artificial Intelligence for Women of Childbearing Age - Obinna Nwokoro, Chukwudi; Inyang, Udoinyang G.; Eyoh, Imo J.; Umoh, U. A.; Augustine Duke, Onyeabochukwu; Nwokoro, Kelechi C.; Opara, Peace I.; Obinachi, Chinmanma; Kingsley, Joseph U. (Asian Journal of Science and Applied Technology, 2023)
Our research has identified a significant shift in the utilization of machine learning models for prediction and decision-making. Rather than solely relying on these models, there is now a growing focus on their explainability and interpretability. Data for this study were acquired between 2019 and 2022 in the Nigerian Coastal Plain, encompassing information from two thousand patients collected at secondary and tertiary healthcare centers, comprising fifteen distinct features. Our team utilized the Shapley additive explanation (SHAP) method to achieve this goal, adhering to the principle of fair play from game theory, giving every maternal attribute used in our work equal consideration. With an RF model and explainable AI techniques, we aimed to predict maternal outcomes and provide comprehensive insights into the most important features of mothers within the bearing age group. We summarized the study’s findings in terms of demography, laboratory results, physical examination, and mode of delivery. Our analysis revealed that mothers of an older age are more likely to experience a caesarean section or have a child with Down syndrome. However, we also found that the SHAP method, along with other XAI methods such as LIME and CIU, can play a vital role in improving satisfaction, time, and understanding. This approach can greatly improve medical decision-making, benefiting both mothers and their children. Our confidence in these findings is high, and we believe they will have a noteworthy impact on the field of parental health.