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Biophysical versus machine learning models for predicting rectal and skin temperatures in older adults
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Zhiwei Xu,
Robert D Meade,
Glen P Kenny,
Sebastian Binnewies,
Aaron J E Bach
Journal of Thermal Biology, Volume: 128, Start page: 104078
Swansea University Author:
Alberto Coccarelli
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© 2025 The Authors. This is an open access article distributed under the terms of the Creative Commons CC-BY license.
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DOI (Published version): 10.1016/j.jtherbio.2025.104078
Abstract
This study compares the efficacy of machine learning models to traditional biophysical models in predicting rectal (T ) and skin (T ) temperatures of older adults (≥60 years) during prolonged heat exposure. Five machine learning models were trained on data using 4-fold cross validation from 162 day-...
| Published in: | Journal of Thermal Biology |
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| ISSN: | 0306-4565 1879-0992 |
| Published: |
Elsevier Ltd
2025
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa69082 |
| Abstract: |
This study compares the efficacy of machine learning models to traditional biophysical models in predicting rectal (T ) and skin (T ) temperatures of older adults (≥60 years) during prolonged heat exposure. Five machine learning models were trained on data using 4-fold cross validation from 162 day-long (8-9h) sessions involving 76 older adults across six environments, from thermoneutral to heatwave conditions. These models were compared to three biophysical models: the JOS-3 model, the Gagge two-node model, and an optimised two-node model. Our findings show that machine learning models, particularly ridge regression, outperformed biophysical models in prediction accuracy. The ridge regression model achieved a Root-Mean Squared Error (RMSE) of 0.27 °C for T , and 0.73 °C for T . Among the best biophysical models, the optimised two-node model achieved an RMSE of 0.40 °C for T , while JOS-3 achieved an RMSE of 0.74 °C for T . Of all models, ridge regression had the highest proportion of participants with T RMSEs within clinically meaningful thresholds at 70% (<0.3 °C) and the highest proportion for T at 88% (<1.0 °C), tied with the JOS-3 model. Our results suggest machine learning models better capture the complex thermoregulatory responses of older adults during prolonged heat exposure. The study highlights machine learning models' potential for personalised heat risk assessments and real-time predictions. Future research should expand upon training datasets, incorporate more dynamic conditions, and validate models in real-world settings. Integrating these models into home-based monitoring systems or wearable devices could enhance heat management strategies for older adults. |
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| Keywords: |
Aged; Body temperature; Extreme heat; Heat stress disorders; Theoretical models |
| College: |
Faculty of Science and Engineering |
| Funders: |
This research was funded by the Wellcome Trust (grant 224709/Z/21/Z: ‘Individualised heat-health early warning systems: A novel digital solution’, held by S. Rutherford, A. J. E. Bach, S. Binnewies). Funding for collection of physiological data was provided by the Canadian Institutes of Health Research (grant #399434 and PJT-180242, held by G. Kenny) and Health Canada (contract #4500387992, held by G. Kenny). |
| Start Page: |
104078 |