Machine Learning-Based Prediction of Heatwave-Related Hospitalizations: A Case Study in Matam, Senegal

This study assesses the impact of heatwaves on hospital admissions in the Matam region of Senegal by combining climatic indices with machine learning methods. Using daily maximum temperature (TMAX) and heat index (HI), heatwave events were identified from 2017 to 2022. Hospital data from Ourossogui Regional Hospital were analyzed, and three predictive models, Random Forest (RF), Extreme Gradient Boosting (XGB), and Generalized Additive Models (GAMs), were compared. A bootstrapping approach with 1000 iterations was used to evaluate model robustness. The findings reveal a significant delayed effect of heatwaves, with increased hospitalizations occurring three to five days after the event. RF outperformed the other models with R<sup>2</sup> values ranging from 0.51 to 0.72. These findings highlight the need to enhance heatwave monitoring and promote the integration of impact-based climate forecasting into health early warning systems, particularly to protect vulnerable groups such as the elderly, children, and outdoor workers.

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Publication Details

Journal
International Journal of Environmental Research and Public Health
Published
2025-08-28
DOI
https://doi.org/10.3390/ijerph22091349
Citations
1
Primary Topic
Climate Change and Health Impacts
Type
article
Field-Weighted Citation Impact
1.20

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article

Machine Learning-Based Prediction of Heatwave-Related Hospitalizations: A Case Study in Matam, Senegal

Papa Ngor Ndiaye, Md Abul Ehsan Bhuiyan, Daouda Badiane, Marie-Jeanne Sambou et al.
1 citations
International Journal of Environmental Research and Public Health
Climate Change and Health Impacts
1.20
article

Machine Learning-Based Prediction of Heatwave-Related Hospitalizations: A Case Study in Matam, Senegal

Papa Ngor Ndiaye, Md Abul Ehsan Bhuiyan, Daouda Badiane, Marie-Jeanne Sambou, Oumou Kalsom Gueye, Ibrahima Sy, E. Bekele, Wassila M. Thiaw, Amadou Gaye, Mory Touré, Ibrahima Diouf, Ousmane Ndiaye, Aïda Diongue‐Niang, Adama Faye
article en
1 citations

Abstract

This study assesses the impact of heatwaves on hospital admissions in the Matam region of Senegal by combining climatic indices with machine learning methods. Using daily maximum temperature (TMAX) and heat index (HI), heatwave events were identified from 2017 to 2022. Hospital data from Ourossogui Regional Hospital were analyzed, and three predictive models, Random Forest (RF), Extreme Gradient Boosting (XGB), and Generalized Additive Models (GAMs), were compared. A bootstrapping approach with 1000 iterations was used to evaluate model robustness. The findings reveal a significant delayed effect of heatwaves, with increased hospitalizations occurring three to five days after the event. RF outperformed the other models with R<sup>2</sup> values ranging from 0.51 to 0.72. These findings highlight the need to enhance heatwave monitoring and promote the integration of impact-based climate forecasting into health early warning systems, particularly to protect vulnerable groups such as the elderly, children, and outdoor workers.

International Journal of Environmental Research and Public HealthVol. 22(9)
Ministère de la Santé Publique et Action Sociale (SN), NOAA National Centers for Environmental Prediction (US), Centre Universitaire de Labé (GN), Université du Sine Saloum El-Hâdj Ibrahima NIASS (SN), Agence Nationale de l'Aviation Civile et de la Météorologie (SN), African Center of Meteorological Application for Development (NE), Centre de Suivi Ecologique (SN), Cheikh Anta Diop University (SN)
African Population and Health Research Center, National Oceanic and Atmospheric Administration
Openalex Percentile: Top 19%
Climate Change and Health Impacts
1.20
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