IMPROVING MATERNAL OUTCOMES: AN ADAPTIVE AND EXPLAINABLE AI SOLUTION FOR MOTHERS IN THE CHILDBEARING AGE

Article in Press

This research harnesses the capabilities of explainable artificial intelligence (XAI) techniques, including Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, and Multi-Layer Perceptron, to advance maternal outcome predictions during pregnancy, with a specific focus on critical determinants such as age, pack cell volume, weight, and maternal blood pressure. Ethical clearance was obtained for comprehensive...

Author(s):Chukwudi Obinna Nwokoro, Parteek Kumar, Faith-Michael Uzoka, Udoinyang G. Inyang, Imo J. Eyoh, Onyeabochukwu Augustine Duke, Kelechi Cynthia. Nwokoro, Obinnachi, Chinmanma and Joseph U. K