Academic Journal
Assessment of socioeconomic and demographic risk factors for low birth weight using model-agnostic explainable ensembles.
| Τίτλος: | Assessment of socioeconomic and demographic risk factors for low birth weight using model-agnostic explainable ensembles. |
|---|---|
| Συγγραφείς: | Hamja MA; Department of Statistics, Hajee Mohammad Danesh Science and Technology University, Dinajpur, 5200, Bangladesh. Electronic address: amirhamja1190@gmail.com., Hasan M; School of Information Technology, Deakin University, Geelong, 3220, Australia. Electronic address: mahmudulmoon123@gmail.com., Jahan M; Dinajpur Nursing College, Dinajpur, 5200, Bangladesh. Electronic address: maknunjahan2001@gmail.com., Hassan MZ; Department of Statistics, Hajee Mohammad Danesh Science and Technology University, Dinajpur, 5200, Bangladesh. Electronic address: nirstathstu@gmail.com. |
| Πηγή: | Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2026 May 15; Vol. 279, pp. 109303. Date of Electronic Publication: 2026 Mar 04. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Scientific Publishers Country of Publication: Ireland NLM ID: 8506513 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7565 (Electronic) Linking ISSN: 01692607 NLM ISO Abbreviation: Comput Methods Programs Biomed Subsets: MEDLINE |
| Imprint Name(s): | Publication: Limerick : Elsevier Scientific Publishers Original Publication: Amsterdam : Elsevier Science Publishers, c1984- |
| Ιατρικοί όροι (MeSH): | Infant, Low Birth Weight* , Socioeconomic Factors* , Demography*, Bangladesh/epidemiology ; Humans ; Female ; Risk Factors ; Infant, Newborn ; Machine Learning ; Adult ; Predictive Learning Models ; Logistic Models ; Random Forest ; Artificial Intelligence ; Bayes Theorem ; Health Surveys ; Ensemble Learning |
| Περίληψη: | Background and Objective: Low birth weight (LBW) is a major global public health concern, strongly linked to neonatal morbidity and long-term health complications. Early prediction of LBW is essential to reduce neonatal mortality and guide targeted healthcare interventions. This study proposes a predictive framework integrating machine learning (ML), deep learning (DL), and model-agnostic eXplainable Artificial Intelligence (XAI) to identify key socioeconomic and demographic determinants of LBW. Methods: Data from the Bangladesh Demographic and Health Survey (BDHS), comprising 1574 participants and 12 variables, are analyzed. Key predictors included maternal age, education, household wealth, geographic region, birth order, and maternal BMI. Chi-square tests assess variable associations. A stacking ensemble model, SmartFusion-LR5, is developed, combining K-Nearest Neighbors, Logistic Regression (LR), Decision Tree, Random Forest, and Naive Bayes, with LR as the meta-learner. Model performance is evaluated using accuracy, precision, recall, area under the curve (AUC), F1-score, and Matthews correlation coefficient (MCC). Results: Significant disparities in LBW prevalence are observed across geographic divisions, with higher parental education and socioeconomic status associated with healthier outcomes. The SmartFusion-LR5 model achieves the highest overall discriminative capability compared to baselines, attaining 93.0% accuracy, 86.7% precision, 99.8% recall, 92.8% F1-score, 94.0% AUC, and an MCC of 86.0%. Comparable performance also obtained from SmartFusion-XGB4 (91.8% accuracy, 86.2% precision, 99.6% recall, 92.4% F1-score, 92.2% AUC, MCC 84.8%) and SmartFusion-RF4 (91.7% accuracy, 86.2% precision, 99.2% recall, 92.2% F1-score, 92.0% AUC, MCC 84.4%). Global XAI methods identified age at first birth, division, residence, wealth, and husband's education as key determinants, while local explanations revealed individual feature impacts. Conclusions: The proposed framework offers a robust, interpretable, and scalable approach for early LBW risk prediction, supporting targeted maternal and child health interventions in resource-constrained settings. (Copyright © 2026 Elsevier B.V. All rights reserved.) |
| Competing Interests: | 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. |
| Contributed Indexing: | Keywords: Explainable ensemble models; Health data analytics; Low birth weight; Model agnostic explainability; Socio-demographic risk factors |
| Entry Date(s): | Date Created: 20260305 Date Completed: 20260710 Latest Revision: 20260710 |
| Update Code: | 20260711 |
| DOI: | 10.1016/j.cmpb.2026.109303 |
| PMID: | 41785538 |
| Βάση Δεδομένων: | MEDLINE |
καταχωρήστε σχόλιο πρώτοι!