EPIDEMIOLOGY AND HEALTH DATA INSIGHTS
Review Article
Dec 30, 2025 177 107

Machine Learning for Predictive Modeling of Climate-Sensitive Autoimmune Diseases

Ikemefula Oriaku 1,
Jennifer Payin Baiden 2,
Oluwafemi Johnson Bamidele 3,
Olukunle O. Akanbi 4,
Vivian Ukamaka Nwokedi 5 *
1 Department of Medical Laboratory Science, University of Nigeria, Nsukka, Nigeria2 Department of Nanoscience and Nanoengineering, University of North Carolina, Greensboro, USA3 Department of Computer Science, College of Engineering, Prairie View A&M University, USA 4 Department of Psychology and Behavioral Sciences, National Louis University, FL, USA5 Department of Clinical Pharmacy, Faculty of Pharmacy, University of Benin, Benin City, Nigeria* Corresponding Author
Epidemiology and Health Data Insights, 2(1), 2026, ehdi025, https://doi.org/10.63946/ehdi/17664
Publication date: Dec 30, 2025
Full Text (PDF)

ABSTRACT

Autoimmune diseases, including Systemic Lupus Erythematosus (SLE), Rheumatoid Arthritis (RA), and Multiple Sclerosis (MS), represent a growing global health burden. These diseases disproportionately affect women and the young, and their complex aetiology involves an interplay between genetic susceptibility and environmental triggers. In light of climate change’s increasing influence on health outcomes, this study explores the potential of machine learning (ML) models to predict climate-sensitive autoimmune diseases. We examine the integration of diverse data sources, such as electronic health records (EHRs), genomic data, and climate exposures, to enhance predictive accuracy. Current ML models in autoimmune disease prediction primarily rely on clinical and omics data, with limited consideration for environmental factors. We identify significant gaps, particularly in incorporating climate data such as particulate matter, UV radiation, and temperature variability. The study also highlights the challenges of data fusion, feature engineering, and causal inference in these models. Ethical concerns, including data privacy, model explainability, and equity, are also addressed. The research underscores the need for large-scale, prospective studies to validate climate-informed models and calls for policy-driven approaches to ensure equitable access and deployment. By bridging these gaps, climate-informed ML models hold promise for personalized, proactive disease prevention and public health planning.

KEYWORDS

Machine Learning Autoimmune Diseases Climate Change Predictive Modeling Environmental Factors

CITATION (Vancouver)

Oriaku I, Baiden JP, Bamidele OJ, Akanbi OO, Nwokedi VU. Machine Learning for Predictive Modeling of Climate-Sensitive Autoimmune Diseases. Epidemiology and Health Data Insights. 2026;2(1):ehdi025. https://doi.org/10.63946/ehdi/17664
APA
Oriaku, I., Baiden, J. P., Bamidele, O. J., Akanbi, O. O., & Nwokedi, V. U. (2026). Machine Learning for Predictive Modeling of Climate-Sensitive Autoimmune Diseases. Epidemiology and Health Data Insights, 2(1), ehdi025. https://doi.org/10.63946/ehdi/17664
Harvard
Oriaku, I., Baiden, J. P., Bamidele, O. J., Akanbi, O. O., and Nwokedi, V. U. (2026). Machine Learning for Predictive Modeling of Climate-Sensitive Autoimmune Diseases. Epidemiology and Health Data Insights, 2(1), ehdi025. https://doi.org/10.63946/ehdi/17664
AMA
Oriaku I, Baiden JP, Bamidele OJ, Akanbi OO, Nwokedi VU. Machine Learning for Predictive Modeling of Climate-Sensitive Autoimmune Diseases. Epidemiology and Health Data Insights. 2026;2(1), ehdi025. https://doi.org/10.63946/ehdi/17664
Chicago
Oriaku, Ikemefula, Jennifer Payin Baiden, Oluwafemi Johnson Bamidele, Olukunle O. Akanbi, and Vivian Ukamaka Nwokedi. "Machine Learning for Predictive Modeling of Climate-Sensitive Autoimmune Diseases". Epidemiology and Health Data Insights 2026 2 no. 1 (2026): ehdi025. https://doi.org/10.63946/ehdi/17664
MLA
Oriaku, Ikemefula et al. "Machine Learning for Predictive Modeling of Climate-Sensitive Autoimmune Diseases". Epidemiology and Health Data Insights, vol. 2, no. 1, 2026, ehdi025. https://doi.org/10.63946/ehdi/17664

REFERENCES

  1. Miller FW. The increasing prevalence of autoimmunity and autoimmune diseases: an urgent call to action for improved understanding, diagnosis, treatment, and prevention. Curr Opin Immunol. 2023 Feb;80:102266. https://doi.org/10.1016/j.coi.2022.102266
  2. Conrad N, Misra S, Verbakel JY, Verbeke G, Molenberghs G, Taylor PN, et al. Incidence, prevalence, and co-occurrence of autoimmune disorders over time and by age, sex, and socioeconomic status: a population-based cohort study of 22 million individuals in the UK. Lancet. 2023 Jun;401(10391):1878-90. https://doi.org/10.1016/S0140-6736(23)00457-9
  3. Zeng SQ, Zhen JH, Pu Y, Liu C, Hu JM, Chen JJ, et al. Global, regional, and national burden of autoimmune disease in older adults (≥ 60 years) from 1990 to 2021: Results from the Global Burden of Disease Study 2021. J Nutr Health Aging. 2025 Nov;29(11):100681. https://doi.org/10.1016/j.jnha.2025.100681
  4. Desai MK, Brinton RD. Autoimmune disease in women: endocrine transition and risk across the lifespan. Front Endocrinol (Lausanne). 2019 Apr;10:265. https://doi.org/10.3389/fendo.2019.00265
  5. Angum F, Khan T, Kaler J, Siddiqui L, Hussain A. The prevalence of autoimmune disorders in women: a narrative review. Cureus. 2020 May;12(5):e8094. https://doi.org/10.7759/cureus.8094
  6. Kumar M, Yip L, Wang F, Marty SE, Fathman CG. Autoimmune disease: genetic susceptibility, environmental triggers, and immune dysregulation. Where can we develop therapies? Front Immunol. 2025 Aug;16:1626082. https://doi.org/10.3389/fimmu.2025.1626082
  7. Yasmeen F, Pirzada RH, Ahmad B, Choi B, Choi S. Understanding autoimmunity: mechanisms, predisposing factors, and cytokine therapies. Int J Mol Sci. 2024 Jul;25(14):7666. https://doi.org/10.3390/ijms25147666
  8. Imberti L, Tiecco G, Logiudice J, Castelli F, Quiros‐Roldan E. Effects of Climate Change on the Immune System: A Narrative Review. Health Sci Rep. 2025 Apr;8(4):e70627. https://doi.org/10.1002/hsr2.70627
  9. Adami G, Pontalti M, Cattani G, Rossini M, Viapiana O, Orsolini G, et al. Association between long-term exposure to air pollution and immune-mediated diseases: a population-based cohort study. RMD Open. 2022 Feb;8(1):e002055. https://doi.org/10.1136/rmdopen-2021-002055
  10. Sangkham S, Phairuang W, Sherchan SP, Pansakun N, Munkong N, Sarndhong K, et al. An update on adverse health effects from exposure to PM2.5. Environ Adv. 2024 Dec;18:100603. https://doi.org/10.1016/j.envadv.2024.100603
  11. Ho WC, Chou LW, Wang RY, Doan TN, Yu HL, Chou TH, et al. Association between exposure to ambient air pollution and the risk of rheumatoid arthritis in Taiwan: A population-based retrospective cohort study. Int J Environ Res Public Health. 2022 Jun;19(12):7006. https://doi.org/10.3390/ijerph19127006
  12. Barbhaiya M, Hart JE, Malspeis S, Tedeschi SK, VoPham T, Sparks JA, et al. Association of ultraviolet B radiation and risk of systemic lupus erythematosus among women in the nurses’ health studies. Arthritis Care Res (Hoboken). 2023 Jul;75(7):1409-15. https://doi.org/10.1002/acr.24974
  13. Christogianni A, Bibb R, Davis SL, Jay O, Barnett M, Evangelou N, et al. Temperature sensitivity in multiple sclerosis: an overview of its impact on sensory and cognitive symptoms. Temperature (Austin). 2018 Jul;5(3):208-23. https://doi.org/10.1080/23328940.2018.1475831
  14. EK KP, PR S, Banerjee M. Impact of seasonal cycle on Rheumatoid Arthritis based on genetic and epigenetic mechanism. Front Immunol. 2025 Sep;16:1601767. https://doi.org/10.3389/fimmu.2025.1601767
  15. Rajula HS, Verlato G, Manchia M, Antonucci N, Fanos V. Comparison of conventional statistical methods with machine learning in medicine: diagnosis, drug development, and treatment. Medicina (Kaunas). 2020 Sep;56(9):455. https://doi.org/10.3390/medicina56090455
  16. Donovan M. The Role of Machine Learning in Enhancing Biomedical Systems for Personalized Medicine. J Biomed Syst Emerg Technol. 2024;11:207.
  17. Xu Y, Wu Q. Using machine learning and single nucleotide polymorphisms for improving rheumatoid arthritis risk Prediction in postmenopausal women. PLOS Digit Health. 2025 Apr;4(4):e0000790. https://doi.org/10.1371/journal.pdig.0000790
  18. Shi Y, Zhou M, Chang C, Jiang P, Wei K, Zhao J, et al. Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management. Front Immunol. 2024 Jun;15:1409555. https://doi.org/10.3389/fimmu.2024.1409555
  19. Forrest IS, Petrazzini BO, Duffy Á, Park JK, O'Neal AJ, Jordan DM, et al. A machine learning model identifies patients in need of autoimmune disease testing using electronic health records. Nat Commun. 2023 Apr;14(1):2385. https://doi.org/10.1038/s41467-023-37996-7
  20. Chung CW, Hsiao TH, Huang CJ, Chen YJ, Chen HH, Lin CH, et al. Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus. BioData Min. 2021 Oct;14(1):52. https://doi.org/10.1186/s13040-021-00284-5
  21. Liu S, Liu Y, Li M, Shang S, Cao Y, Shen X, et al. Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades. Front Immunol. 2025 Feb;16:1525462. https://doi.org/10.3389/fimmu.2025.1525462
  22. Stafford IS, Kellermann M, Mossotto E, Beattie RM, MacArthur BD, Ennis S. A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ Digit Med. 2020 Mar;3(1):30. https://doi.org/10.1038/s41746-020-0229-3
  23. Xu P, Cai J, Gao Y, Rong Z. MIRACLE: Multi-task learning based interpretable regulation of autoimmune diseases through common latent epigenetics. arXiv [Preprint]. 2023 Jun. Available from: https://doi.org/10.48550/arXiv.2306.13866
  24. Momtazmanesh S, Nowroozi A, Rezaei N. Artificial intelligence in rheumatoid arthritis: current status and future perspectives: a state-of-the-art review. Rheumatol Ther. 2022 Oct;9(5):1249-304. https://doi.org/10.1007/s40744-022-00475-4
  25. Kruta J, Carapito R, Trendelenburg M, Martin T, Rizzi M, Voll RE, et al. Machine learning for precision diagnostics of autoimmunity. Sci Rep. 2024 Nov;14(1):27848. https://doi.org/10.1038/s41598-024-76093-7
  26. Abbas JK, Hatem KK, Hussein WA. Use of artificial intelligence in diagnosis of various autoimmune diseases, a comprehensive literature review about current situation & future directions. Afr J Biomed Res. 2025 Jan;28(1s):952-61. https://doi.org/10.53555/AJBR.v28i1S.6291
  27. White-Newsome JL, Brines SJ, Brown DG, Dvonch JT, Gronlund CJ, Zhang K, et al. Validating Satellite-Derived Land Surface Temperature with in Situ Measurements: A Public Health Perspective. Environ Health Perspect. 2013 Aug;121(8):925-31. https://doi.org/10.1289/ehp.1206176
  28. Agache I, Akdis C, Akdis M, Al-Hemoud A, Annesi-Maesano I, Balmes J, et al. Immune-mediated disease caused by climate change-associated environmental hazards: mitigation and adaptation. Front Sci. 2024 Feb;2:1279192. https://doi.org/10.3389/fsci.2024.1279192
  29. Tamang S. Public Health in the Age of AI: How Synthetic Patients Are Helping Uncover Environmental Drivers of Autoimmune & Rheumatic Disease. Stanford Medicine News [Internet]. 2025. Available from: https://med.stanford.edu/medicine/news/current-news/standard-news/synthetic-patients.html
  30. Ku Y, Kwon SB, Yoon JH, Mun SK, Chang M. Machine learning models for predicting the occurrence of respiratory diseases using climatic and air-pollution factors. Clin Exp Otorhinolaryngol. 2022 May;15(2):168-76. https://doi.org/10.21053/ceo.2021.01536
  31. Bae S, Kim HC, Ye B, Choi WJ, Hong YS, Ha M. Causal inference in environmental epidemiology. Environ Health Toxicol. 2017 Oct;32:e2017015. https://doi.org/10.5620/eht.e2017015
  32. Amnuaylojaroen T, Parasin N. A Machine Learning Perspective on the Climatic and Socioeconomic Determinants of Mental Health in Southeast Asia. World. 2025 Apr;6(2):48.
  33. Casey JA, Daouda M, Babadi RS, Do V, Flores NM, Berzansky I, et al. Methods in public health environmental justice research: a scoping review from 2018 to 2021. Curr Environ Health Rep. 2023 Sep;10(3):312-36. https://doi.org/10.1007/s40572-023-00406-7
  34. Cardona OD, Van Aalst MK, Birkmann J, Fordham M, Mc Gregor G, Rosa P, et al. Determinants of risk: exposure and vulnerability. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, editors. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 2012. p. 65-108. https://doi.org/10.1017/CBO9781139177245.005
  35. Miller FW, Alfredsson L, Costenbader KH, Kamen DL, Nelson LM, Norris JM, et al. Epidemiology of environmental exposures and human autoimmune diseases: findings from a National Institute of Environmental Health Sciences Expert Panel Workshop. J Autoimmun. 2012 Dec;39(4):259-71. https://doi.org/10.1016/j.jaut.2012.05.002
  36. Abdullahi T, Nitschke G. Predicting disease outbreaks with climate data. ClimaHealth [Internet]. 2021. Available from: https://climahealth.info/resource-library/predicting-disease-outbreaks-with-climate-data/
  37. Rosenbaum PR, Rubin DB. Propensity scores in the design of observational studies for causal effects. Biometrika. 2023 Mar;110(1):1-3. https://doi.org/10.1093/biomet/asac054
  38. Zhao Y. The application of propensity score methods in observational studies. Front Appl Math Stat. 2024 Apr;10:1384217. https://doi.org/10.3389/fams.2024.1384217
  39. VanderWeele TJ. Principles of confounder selection. Eur J Epidemiol. 2019 Mar;34(3):211-9. https://doi.org/10.1007/s10654-019-00494-6
  40. Walter TG, Bricknell LK, Preston RG, Crawford EG. Climate change adaptation methods for public health prevention in Australia: an integrative review. Curr Environ Health Rep. 2024 Mar;11(1):71-87. https://doi.org/10.1007/s40572-023-00422-7
  41. Bianco G, Espinoza-Chávez RM, Ashigbie PG, Junio H, Borhani C, Miles-Richardson S, et al. Projected impact of climate change on human health in low-and middle-income countries: a systematic review. BMJ Glob Health. 2024 Mar;8(Suppl 3):e015550. https://doi.org/10.1136/bmjgh-2024-015550
  42. Yenew C, Bayeh GM, Gebeyehu AA, Enawgaw AS, Asmare ZA, Ejigu AG, et al. Scoping review on assessing climate-sensitive health risks. BMC Public Health. 2025 Mar;25(1):914. https://doi.org/10.1186/s12889-025-22148-x
  43. Yadav N, Pandey S, Gupta A, Dudani P, Gupta S, Rangarajan K. Data Privacy in Healthcare: In the Era of Artificial Intelligence. Indian Dermatol Online J. 2023 Nov-Dec;14(6):788-92. https://doi.org/10.4103/idoj.idoj_543_23
  44. Chaddad A, Lu Q, Li J, Katib Y, Kateb R, Tanougast C, et al. Explainable, domain-adaptive, and federated artificial intelligence in medicine. IEEE/CAA J Autom Sin. 2023 Apr;10(4):859-76. https://doi.org/10.48550/arXiv.2211.09317
  45. Skevaki C, Nadeau KC, Rothenberg ME, Alahmad B, Mmbaga BT, Masenga GG, et al. Impact of climate change on immune responses and barrier defense. J Allergy Clin Immunol. 2024 May;153(5):1194-205. https://doi.org/10.1016/j.jaci.2024.01.016

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