Authors : Alhaji Osman Bah; Abdul Hamid Turay; Iyeba Alpha Kallon; Alhajie Mohamed Sesay

Volume/Issue : Volume 11 - 2026, Issue 10 - October

Google Scholar : https://tinyurl.com/bdfbnutz

DOI : https://doi.org/10.38124/ijisrt/26Oct769

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Maternal health risk prediction can support the early identification of pregnancies that may require closer clinical attention, particularly in resource-constrained settings. This study evaluates seven supervised machine-learning classifiers— Logistic Regression, Support Vector Machine (SVM) with Polynomial and Radial Basis Function (RBF) kernels, Gradient Boosting Machine (GBM), XGBoost, LightGBM, and CatBoost—for three-class maternal health-risk classification. The primary analysis uses a 500-record dataset obtained from Poly Clinic, Peninsula Road, Sierra Leone, containing eight predictors: age, systolic blood pressure, diastolic blood pressure, blood glucose, BMI, heart rate, parity, and hemoglobin.

Keywords : Maternal Health, Machine Learning, Maternal Risk Prediction, Logistic Regression, Support Vector Machine, Gradient Boosting, XGBoost, LightGBM, CatBoost, SMOTE, Sierra Leone.

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