A Practical Framework for Machine Learning Model Selection in Global Health Research: Balancing Performance, Interpretability, and Resource Constraints in Low- and Middle-Income Countries
Document Type : Review
Authors
- 1 Department of Planning, Research and Statistics, Federal Neuropsychiatric Hospital Dawanau, Kano, Nigeria.
Abstract
Objective
To develop and propose a pragmatic, evidence-based framework for selecting machine learning (ML) models in low- and middle-income countries (LMICs), balancing predictive performance, interpretability, resource constraints, and fairness to enable ethical and feasible adoption in global health research.
Methods and Analysis
Evidence was synthesized from systematic reviews on ML performance, TRIPOD+AI reporting standards, and clinical AI fairness gap analyses. This informed a six-step framework: (1) dataset characterization, (2) interpretability assessment, (3) resource evaluation, (4) performance benchmarking, (5) fairness evaluation, and (6) transparent reporting. The framework operationalizes decision thresholds grounded in the literature, including a 5,000-observation complexity threshold and a 0.05 fairness gap benchmark. Decision tools—including a standardized table and a logically branched flowchart—guide context-sensitive choices to prioritize clinical utility and local feasibility.
Results
The six-step framework elucidates trade-offs among model complexity, interpretability, and resource needs (categorized as "LMIC Fit"). Application of the framework suggests that for tabular health data, parsimonious models often achieve comparable performance to complex ensembles while ensuring superior transparency. The framework mandates 10-fold cross-validation and 95% confidence intervals to ensure reproducibility, alongside mandatory adherence to global reporting standards.
Conclusion
Model selection in global health is not merely a technical task but a balance of performance, equity, and resources. The best choice of methodology is the most parsimonious model that meets performance requirements without compromising fairness or local feasibility. This framework provides researchers with a defensible, evidence-based tool to navigate these trade-offs, fostering the adoption of AI that is transparent, reproducible, and contextually appropriate for LMIC settings.
To develop and propose a pragmatic, evidence-based framework for selecting machine learning (ML) models in low- and middle-income countries (LMICs), balancing predictive performance, interpretability, resource constraints, and fairness to enable ethical and feasible adoption in global health research.
Methods and Analysis
Evidence was synthesized from systematic reviews on ML performance, TRIPOD+AI reporting standards, and clinical AI fairness gap analyses. This informed a six-step framework: (1) dataset characterization, (2) interpretability assessment, (3) resource evaluation, (4) performance benchmarking, (5) fairness evaluation, and (6) transparent reporting. The framework operationalizes decision thresholds grounded in the literature, including a 5,000-observation complexity threshold and a 0.05 fairness gap benchmark. Decision tools—including a standardized table and a logically branched flowchart—guide context-sensitive choices to prioritize clinical utility and local feasibility.
Results
The six-step framework elucidates trade-offs among model complexity, interpretability, and resource needs (categorized as "LMIC Fit"). Application of the framework suggests that for tabular health data, parsimonious models often achieve comparable performance to complex ensembles while ensuring superior transparency. The framework mandates 10-fold cross-validation and 95% confidence intervals to ensure reproducibility, alongside mandatory adherence to global reporting standards.
Conclusion
Model selection in global health is not merely a technical task but a balance of performance, equity, and resources. The best choice of methodology is the most parsimonious model that meets performance requirements without compromising fairness or local feasibility. This framework provides researchers with a defensible, evidence-based tool to navigate these trade-offs, fostering the adoption of AI that is transparent, reproducible, and contextually appropriate for LMIC settings.
Keywords
Global health
Low- And Middle-Income Countries
Machine Learning
model selection
Resource Constraints
TRIPOD+AI