Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Multidisciplinary Science: MIKAILALSYS
Olutola, Agbelusi
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Environmental Science Physics Social Sciences Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Ensemble-Based Model for Predicting Maternal Health Risk in Southwest Nigeria Olutola, Agbelusi
Journal of Multidisciplinary Science: MIKAILALSYS Vol 3 No 2 (2025): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v3i2.6150

Abstract

Maternal mortality remains a critical public health concern, particularly in Nigeria, which continues to report one of the highest maternal death rates globally. This study proposes an innovative approach for predicting maternal health risks by integrating primary clinical data with a rule-based classification system and ensemble machine learning techniques. A dataset of 148 records was obtained from Ondo State University Teaching Hospital, encompassing key maternal health indicators. Given the lack of predefined class labels, a rule-based labeling framework adapted from a publicly available Kaggle dataset was applied. The data underwent preprocessing, including imputation for missing values and balancing using the Synthetic Minority Over-sampling Technique (SMOTE). Three ensemble machine learning models Voting, Stacking, and Bagging were developed and evaluated based on accuracy, precision, recall, and F1-score. Results showed that SMOTE markedly enhanced classification performance, with the Stacking ensemble achieving the highest accuracy (94.6%) and precision (97.1%). These outcomes highlight the potential of machine learning to enable early detection of maternal health risks and support improved decision-making in clinical settings.
Co-Authors