Artificial Intelligence and Machine Learning 2024 (TAIML 2024)

Title: PREDICTING MATERNAL OUTCOMES USING TREE-BASED METHODS IN MACHINE LEARNING

Abstract

Maternal health, with its global significance for maternal mortality rates, is a paramount concern. This study focuses on leveraging tree-based algorithms to aid healthcare providers in informed decision-making for expectant mothers. Analyzing 4,000 antenatal care records in Nigeria's Niger Delta area (2018-2022) identified 15 critical features using Principal Component Analysis (PCA) to predict outcomes like stillbirth, full-term birth, preterm birth, miscarriage, placenta previa, and maternal mortality. Decision Tree (DT) prioritizes Hemoglobin Level (HL), Random Forest (RF) includes HL, Pulse Rate (PR), and Packed Cell Volume Level (PCVL). AdaBoost (ADA) emphasizes HL, Maternal Weight (MW), and Preeclampsia (PREE). Gradient Boosted Trees (GBT) consistently prioritize HL, PREE, and MW, with Extreme Gradient Boosting (XGB) aligning with these features. A bar chart illustrates precision scores, with XGBoost leading at 0.95, GBT at 0.93, Random Forest at 0.92, AdaBoost at 0.91, and DT at 0.90. These findings offer valuable insights for healthcare professionals and researchers aiming to enhance maternal health outcomes. Future research avenues include exploring the synergy of tailored logistic regression models with gradient-boosted algorithms to enhance discrimination and calibration. Additionally, combining gradient-boosted tree algorithms with SHAP (Shapley Additive Explanations) could provide deeper insights into the importance of features and predictive performance improvements. Keywords: Maternal health, Maternal outcomes, Tree-based algorithms, Pregnancy, Maternal mortality.

Biography

Chukwudi Obinna Nwokoro holds a B.Tech. Degree in Mathematics and Computer Science and an MSc in Information Management Technology from the Federal University of Owerri (FUTO) since 2015. He recently completed his Ph.D. in Computational Intelligence at the University of Uyo, Nigeria. Nwokoro's research focuses on medical informatics, including data mining, artificial intelligence, algorithms, and analysis. He has published more than 25 papers extensively in national and international journals, contributing significantly to academia. Apart from his scholarly pursuits, Nwokoro is involved in the industrial sector and mentors research students, guiding them through their academic journey.