• Machine learning prediction of...

Treffer: Machine learning prediction of kangaroo mother care in Sierra Leone: a comparative study of feature selection techniques and classification algorithms.

Title:
Machine learning prediction of kangaroo mother care in Sierra Leone: a comparative study of feature selection techniques and classification algorithms.
Authors:
Osborne A; Institute for Development, Western Area, Freetown, the Republic of Sierra Leone., Soladoye AA; Department of Computer Engineering, Federal University, Oye-Ekiti, Nigeria; Department of Computer Engineering, Adeleke University, Ede, Nigeria., Usani KO; Department of Data Science and Artificial Intelligence, School of Business, Computing and Social Sciences, University of Gloucestershire, Cheltenham, United Kingdom., Adekoya AI; Department of Computer Science, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham, United Kingdom,; Department of Physiology, Faculty of Basic Medical Sciences, University of Ibadan, Ibadan, Nigeria., Wada OZ; College of Science and Engineering, Division of Sustainable Development, Hamad Bin Khalifa University, Qatar Foundation, Education City, Doha, Qatar., Olawade DB; Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Department of Public Health, York St John University, London, United Kingdom; School of Health and Care Management, Arden University, Arden House, Middlemarch Park, Coventry CV3 4FJ, United Kingdom. Electronic address: d.olawade@uel.ac.uk.
Source:
International journal of medical informatics [Int J Med Inform] 2026 Feb; Vol. 206, pp. 106166. Date of Electronic Publication: 2025 Oct 26.
Publication Type:
Journal Article; Comparative Study
Language:
English
Journal Info:
Publisher: Elsevier Science Ireland Ltd Country of Publication: Ireland NLM ID: 9711057 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8243 (Electronic) Linking ISSN: 13865056 NLM ISO Abbreviation: Int J Med Inform Subsets: MEDLINE
Imprint Name(s):
Original Publication: Shannon, Co. Clare, Ireland : Elsevier Science Ireland Ltd., c1997-
MeSH Terms:
Kangaroo-Mother Care Method*/statistics & numerical data , Machine Learning* , Algorithms*, Humans ; Sierra Leone ; Infant, Newborn ; Female ; Adult ; Infant ; Support Vector Machine ; Classification Algorithms
Contributed Indexing:
Keywords: Ensemble methods; Feature selection; Kangaroo mother care; Machine learning; Maternal health
Entry Date(s):
Date Created: 20251101 Date Completed: 20251124 Latest Revision: 20251124
Update Code:
20251125
DOI:
10.1016/j.ijmedinf.2025.106166
PMID:
41175843
Database:
MEDLINE

Weitere Informationen

Background: Kangaroo Mother Care (KMC) is a critical intervention for improving neonatal outcomes, particularly for low-birth-weight infants. Identifying predictors of KMC practice remains essential for targeted health interventions and policy development.
Objective: This study utilizes data from the 2019 Sierra Leone demographic and health survey to identify predictors of KMC using different feature selection techniques and classification algorithms.
Methods: We analyzed 7,377 maternal and child health records from the 2019 Sierra Leone demographic and health survey, applying three feature selection techniques and seven classification algorithms. Data preprocessing included class balancing and cross-validation. Three feature selection techniques employed were: Adaptive Ant Colony Optimization (ACO), Recursive Feature Elimination (RFE), and Backward Feature Selection. Seven machine learning algorithms implemented were: Logistic Regression, Support Vector Machine variants, K-Nearest Neighbours, Random Forest, XGBoost, Stacking Ensemble, and Voting Ensemble. Data preprocessing included SMOTE for class imbalance, 5-fold and 10-fold cross-validation, and hyperparameter optimization using GridSearchCV.
Results: Random Forest and XGBoost consistently achieved the highest performance across all feature selection methods. Using consensus features from multiple selection techniques, Random Forest achieved an accuracy of 0.72, F1-score of 0.78, and ROC-AUC of 0.7689, whilst XGBoost demonstrated similar performance (accuracy: 0.72, F1-score: 0.78, ROC-AUC: 0.7685). Backward Feature Selection and ACO outperformed RFE in identifying discriminative features. Ensemble methods showed robust generalization capabilities.
Conclusion: Machine learning models, particularly ensemble methods combined with comprehensive feature selection techniques, demonstrate strong predictive capability for KMC practice, offering valuable insights for maternal and child health interventions in Sierra Leone.
(Copyright © 2025 The Author(s). Published by Elsevier B.V. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.