A Systematic Review of Machine Learning Approaches for Predicting Heat-Related Skin Diseases under Climate Change
Article Sidebar
Main Article Content
Climate change has intensified the frequency, duration, and severity of extreme heat events, posing significant risks to human health, particularly through the increasing burden of heat-related skin diseases. Elevated temperatures, prolonged ultraviolet radiation exposure, air pollution, and changing environmental conditions have contributed to the rising incidence of conditions such as heat rash, sunburn, skin infections, dermatitis, and skin cancer, with children and older adults being especially vulnerable. Recent advances in machine learning (ML) have enabled the development of predictive models for disease diagnosis and health risk assessment; however, existing studies predominantly focus either on general heat-related health outcomes or on skin disease classification using clinical images, with limited integration of climatic, environmental, and demographic factors for forecasting heat-related skin disease risk under future climate scenarios. The survey presents a comprehensive review of the current literature on climate-driven heat-related skin diseases and the application of machine learning techniques for their prediction. The review examines major climatic and environmental determinants, vulnerable population groups, commonly used datasets, feature selection methods, and predictive algorithms, including Random Forest, Support Vector Machine, Decision Tree, Artificial Neural Networks, Gradient Boosting, and Deep Learning models. It further compares model performance, interpretability, and limitations. The survey highlights emerging research directions for developing robust and interpretable predictive frameworks capable of forecasting future heat-related skin disease risks under climate change.
Downloads
References
Dirk Roosterman, Tobias Goerge, Stefan W. Schneider, Nigel W. Bunnett, and Martin Steinhoff, “Neuronal Control of Skin Function: The Skin as a Neuroimmunoendocrine Organ”, Physiological Reviews 2006 86:4, 1309-1379 10.1152/physrev.00026.2005
Oláh, A., Szöllősi, A.G., Bíró, T. (2012). The Channel Physiology of the Skin. In: Nilius, B., et al. Reviews of Physiology, Biochemistry and Pharmacology, Vol. 163. Reviews of Physiology, Biochemistry and Pharmacology, vol 163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/112_2012_7
Goldust, M. and Grant-Kels, J.M. (2024), Using AI to help address skin health challenges caused by climate change. Int J Dermatol, 63: e126-e127. https://doi.org/10.1111/ijd.17222
Karolina Mieczkowska, Thomas Stringer, John S. Barbieri, Mary Williams, Misha Rosenbach, Surveying the attitudes of dermatologists regarding climate change, British Journal of Dermatology, Volume 186, Issue 4, 1 April 2022, Pages 748–750, https://doi.org/10.1111/bjd.20900
Shen CZ, Zhao AT, Rotemberg V, Daneshjou R, Parker ER, Rosenbach M. Artificial intelligence in dermatology: Clinical promise and environmental impact. J Invest Dermatol. 2026 Jun 10:S0022-202X(26)01043-2. doi: 10.1016/j.jid.2026.03.039. Epub ahead of print. PMID: 42274450.
Chaisurin P, Tantranont K. Examining the Interplay Between Occupational Health Hazards and Health Status Related to Risk Among Home-Based Workers in Thailand. Workplace Health Saf. 2026 Jul;74(7):364-373. doi: 10.1177/21650799261421553. Epub 2026 Mar 16. PMID: 41834727.
Maini Chen, Xiangyu Li, Anrong Dang, Yang Weng, Shi Qiu, Adapting to heatwaves: Optimizing urban green spaces in Beijing to reduce heat health risks, Sustainable Cities and Society, Volume 130, 2025, 106600, ISSN 2210-6707, https://doi.org/10.1016/j.scs.2025.106600.
Oka, K. (2023). Heat-Related Health Impacts of Climate Change and Adaptation Strategies in Japan. In: Akhtar, R. (eds) Climate Change and Human Health Scenarios. Global Perspectives on Health Geography. Springer, Cham. https://doi.org/10.1007/978-3-031-38878-1_5
Wabnitz, K.J.P., Leppmeier, L., Kuempfel, R. et al. Heat, health and inequalities in the WHO European region – a scoping review with an intersectional lens. Int J Equity Health 25, 60 (2026). https://doi.org/10.1186/s12939-026-02787-1
Gerardo Sanchez Martinez, Vladimir Kendrovski, Miguel Antonio Salazar, Francesca de’Donato, Melanie Boeckmann, Heat-health action planning in the WHO European Region: Status and policy implications, Environmental Research, Volume 214, Part 1, 2022, 113709, ISSN 0013-9351, https://doi.org/10.1016/j.envres.2022.113709.
A. Rahman and M. G. Rabiul Alam, "Explainable AI based Maternal Health Risk Prediction using Machine Learning and Deep Learning," 2023 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA, 2023, pp. 0013-0018, doi: 10.1109/AIIoT58121.2023.10174540.
Kute, S.S., Shreyas Madhav, A.V., Kumari, S. and Aswathy, S.U. (2022). Machine Learning–Based Disease Diagnosis and Prediction for E-Healthcare System. In Advanced Analytics and Deep Learning Models (eds A. Mire, S. Malik and A.K. Tyagi). https://doi.org/10.1002/9781119792437.ch6.
Moulaei, K., Afshari, L., Moulaei, R. et al. Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models. Sci Rep 14, 31392 (2024). https://doi.org/10.1038/s41598-024-82931-5
Elliot Mbunge, John Batani,Application of deep learning and machine learning models to improve healthcare in sub-Saharan Africa: Emerging opportunities, trends and implications,Telematics and Informatics Reports,Volume 11,2023,100097,ISSN 2772-5030,https://doi.org/10.1016/j.teler.2023.100097.
Jaiyeoba, O., Ogbuju, E., Ataguba, G. et al. State-of-the-art skin disease classification: a review of deep learning models. Netw Model Anal Health Inform Bioinforma 14, 9 (2025). https://doi.org/10.1007/s13721-024-00495-w
Sultana, N. (2022), Predicting sun protection measures against skin diseases using machine learning approaches. J Cosmet Dermatol, 21: 758-769. https://doi.org/10.1111/jocd.14120
A. Mohanty, A. Sutherland, M. Bezbradica and H. Javidnia, "Skin Disease Analysis With Limited Data in Particular Rosacea: A Review and Recommended Framework," in IEEE Access, vol. 10, pp. 39045-39068, 2022, doi: 10.1109/ACCESS.2022.3165574.
Gulhan Bizel , Albert Einstein, Amey G Jaunjare, sharath kumar Jagannathan, Machine Learning Study: Identification of Skin Diseases for Various Skin Types Using Image Classification. Vol. 2 No. 1 (2024): JBDAI Second Volume. DOI: https://doi.org/10.54116/jbdai.v2i1.32
LEE, J., KWON, K.. Skin health response to climate change weather tailored cosmetics using artificial intelligence. Journal of Medical Artificial Intelligence, North America, 7, jun. 2024. Available at: <https://jmai.amegroups.org/article/view/9056>. Date accessed: 28 Jun. 2026.
Shaik Abdul Khalandar Basha, P. M. Durai Raj Vincent, K. Srividya, Pusarla Samyuktha, Jessu Madhumathi, "A Novel Approach to Analysis Consequence of Climate changes on Erythemato-squamous Diseases using Machine Learning Algorithms," International Journal of Engineering Trends and Technology (IJETT), vol. 70, no. 11, pp. 10-18, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P202
Jérémie Boudreault, Félix Lamothe, Céline Campagna, Fateh Chebana,Machine learning for modelling the health impacts of extreme heat: A comprehensive literature review,Environment International,Volume 206,2025,109965,ISSN 0160-4120,https://doi.org/10.1016/j.envint.2025.109965.
Ssebyala SN, Kintu TM, Muganzi DJ, Dresser C, Demetres MR, Lai Y, et al. (2024) Use of machine learning tools to predict health risks from climate-sensitive extreme weather events: A scoping review. PLOS Clim 3(1): e0000338. https://doi.org/10.1371/journal.pclm.0000338
R. G. Tiwari, T. E. Nyamasvisva, N. Ibrahim, A. K. Agarwal, N. Rakesh and M. Gulhane, "Climate-Aware Healthcare: Predictive Modeling of Disease Outbreaks using Multimodal Data," 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Tirupur, India, 2025, pp. 1811-1816, doi: 10.1109/ICIMIA67127.2025.11200760.
Connor Forbes, Alberto Coccarelli, Zhiwei Xu, Robert D. Meade, Glen P. Kenny, Sebastian Binnewies, Aaron J.E. Bach,Biophysical versus machine learning models for predicting rectal and skin temperatures in older adults,Journal of Thermal Biology,Volume 128,2025,104078,ISSN 0306-4565,https://doi.org/10.1016/j.jtherbio.2025.104078.
Islam, S., Sinha, P., Srivastava, R.K., Khare, M. (2025). Heat Waves and Heat Stress in the Changing Climate: A Data-Driven Evaluation. In: Srivastava, R.K., Chakraborty, A. (eds) Mitigation and Adaptation Strategies Against Climate Change in Natural Systems. Environmental Earth Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-75968-0_14
Javari, P., Javari, M. Heat stress and cardiovascular hospitalizations in a semi-arid megacity: a multi-method epidemiological and machine-learning analysis in Isfahan, Iran. BMC Public Health 26, 1719 (2026). https://doi.org/10.1186/s12889-026-26909-0
Aldosary, A.S., Al-Ramadan, B., Kafy, A.A. et al. Forecasting climate risk and heat stress hazards in arid ecosystems: Machine learning and ensemble models for specific humidity prediction in Dammam, Saudi Arabia. Nat Hazards 121, 9281–9309 (2025). https://doi.org/10.1007/s11069-025-07140-3
Hirano, Y., Kondo, Y., Hifumi, T. et al. Machine learning-based mortality prediction model for heat-related illness. Sci Rep 11, 9501 (2021). https://doi.org/10.1038/s41598-021-88581-1
Sousan S, Mizelle E, Wu Q, Wu R, Iverson G, Popoviciu C, Hemedinger D, Loyola E, Hood T, Ramirez M, Balanay JA. Assessing the Feasibility of Wearable Devices for Physiological Monitoring and Heat Risk Prediction in Outdoor Agricultural Workers. Am J Ind Med. 2026 Jun 9. doi: 10.1002/ajim.70103. Epub ahead of print. PMID: 42265825.
Yuemei Wang, Haoyu Chang, Zhiwei Lian,Evaluation of the feasibility of using skin temperature to predict overall thermal sensation in non-uniform thermal environments,Journal of Thermal Biology,Volume 106,2022,103254,ISSN 0306-4565,https://doi.org/10.1016/j.jtherbio.2022.103254.
K. Tikarya, Y. V. Jain and D. Bhise, "A review: Cattle Breed and Skin Disease Identification Using Deep Learning," 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, 2023, pp. 835-842, doi: 10.1109/ICCCIS60361.2023.10425776.
Anderson, A., Bruce, F., Soyer, H.P., Williams, C. and Saunderson, R.B. (2023), The impact of climate change on skin health. Med J Aust, 218: 388-390. https://doi.org/10.5694/mja2.51931
Alzaeemi, S., Tay, K.G., Ali, G.A. et al. A website-based Skin Disease Identification using a Convolutional Neural Network for Childcare Applications. Iran J Sci Technol Trans Electr Eng (2026). https://doi.org/10.1007/s40998-026-01018-1
Yincai Wu, Xintu Lin, Lily Chen, Diansong Gan, Rujian Li, Yuejun Liu, Lijun Song, Xihai Hao, Tungalag Dong, Linze Liu, Fenglong Lin, Shenglong Wang,Preparation a skin disease UV protection polylactic acid film and crystallinity, mechanical properties characterization,Materials Today Communications,Volume 30,2022,103085,ISSN 2352-4928,https://doi.org/10.1016/j.mtcomm.2021.103085.
Lee, J.; Kwon, K.H. Sustainable Countermeasures for Skin Health Improvement for Green Consumers: The Utilization of Hsian-Tsao during Global Warming. Sustainability 2023, 15, 14619. https://doi.org/10.3390/su151914619
A. A. Nafea, S. A. Alameri, R. R. Majeed, M. A. Khalaf, and M. M. AL-Ani , Trans., “A Short Review on Supervised Machine Learning and Deep Learning Techniques in Computer Vision ”, Babylonian Journal of Machine Learning, vol. 2024, pp. 48–55, Feb. 2024, doi: 10.58496/BJML/2024/004.
M. Bkassiny, Y. Li and S. K. Jayaweera, "A Survey on Machine-Learning Techniques in Cognitive Radios," in IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1136-1159, Third Quarter 2013, doi: 10.1109/SURV.2012.100412.00017.
Ramu, P., Thananjayan, P., Acar, E. et al. A survey of machine learning techniques in structural and multidisciplinary optimization. Struct Multidisc Optim 65, 266 (2022). https://doi.org/10.1007/s00158-022-03369-9
Emmanuel G. Pintelas, Theodore Kotsilieris, Ioannis E. Livieris, and Panagiotis Pintelas. 2018. A review of machine learning prediction methods for anxiety disorders. In Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI '18). Association for Computing Machinery, New York, NY, USA, 8–15. https://doi.org/10.1145/3218585.3218587
Mohanty, A., Gao, G. A survey of machine learning techniques for improving Global Navigation Satellite Systems. EURASIP J. Adv. Signal Process. 2024, 73 (2024). https://doi.org/10.1186/s13634-024-01167-7
R. Reddy and G. K. Shyam, "Analysis Through Machine Learning Techniques: A Survey," 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), Bangalore, India, 2018, pp. 542-546, doi: 10.1109/ICGCIoT.2018.8753050.
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.