Keyword: Environmental Factors

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.