Temporal Validation Changes the Apparent Public-Health Utility of Under-Five Mortality Prediction in Bangladesh: A Four-Round DHS Machine-Learning Study
arXiv:2602.03957v2 Announce Type: replace Abstract: Background: Under-five mortality in Bangladesh remains uneven despite national progress. DHS-based prediction models may guide targeted follow-up, but only if validation reflects future use. We examined how validation design changes apparent prediction performance. Methods: Four BDHS rounds (2011-2022; 33,962 children; 1,290 deaths) were analysed with a 26-feature pipeline and three model classes under four validation regimes, including cross-survey temporal validation (train 2011+2014, calibrate 2017, test 2022). A 32-unit ELU multilayer per
This research reflects ongoing efforts to leverage AI and machine learning for public health improvements, particularly in regions with prevalent health challenges.
It highlights the critical importance of robust validation methods in AI models for public health, ensuring real-world utility and avoiding misleading performance metrics.
The focus shifts from raw prediction accuracy to practical public health utility, emphasizing that appropriate temporal validation is crucial for deploying effective AI solutions in this domain.
- · Public health organizations
- · AI ethicists
- · Population health researchers
- · Developers of poorly validated AI models
- · Stakeholders who prematurely deploy unvalidated AI systems
Improved accuracy and reliability of AI tools for targeted public health interventions.
Increased trust and adoption of machine learning in humanitarian and development sectors due to demonstrable real-world utility.
Potential for more equitable and effective allocation of health resources in developing nations, leading to measurable reductions in preventable mortality.
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Read at arXiv cs.LG