Artificial Intelligence-Driven Prediction of Glucagon-Like-Peptide-1 Receptor Agonist Eligibility in Obesity

Background: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are highly effective pharmacotherapies for obesity, offering substantial weight loss and improvement in obesity-related complications. However, their high cost and limited insurance coverage necessitate precise, data-driven strategies for patient selection. Objective: To develop and validate machine learning (ML) models that predict eligibility for GLP-1 RA therapy using body mass index (BMI) and comorbidity profiles, leveraging both synthetic and real-world patient data. Methods: A rule-based algorithm, grounded in clinical guidelines, was used to generate a synthetic training dataset ( n = 256), while a real-world test set ( n = 287) was derived from electronic health records. Patients were classified as eligible for GLP-1 RA therapy or recommended for generic alternatives based on BMI and major obesity-related complications (e.g., type 2 diabetes, cardiovascular disease, metabolic dysfunction-associated steatohepatits [MASH], sleep apnea, transplant status). Four models, baseline classifier, logistic regression, decision tree, and random forest, were trained and evaluated using cross-validation and independent testing. The rule-based algorithm was also applied directly to the test set. Results: Decision tree, random forest, and logistic regression models achieved near-perfect accuracy (≥99%) on both datasets. Key predictors of eligibility included type 2 diabetes, cardiovascular disease, MASH, sleep apnea, and transplant status. The rule-based model also performed comparably well, demonstrating its clinical validity and interpretability. Conclusions: ML and rule-based models can accurately identify patients eligible for GLP-1 RA therapy, supporting scalable, equitable, and cost-conscious obesity treatment strategies. These tools offer a transparent framework for optimizing access to high-cost antiobesity medications in real-world clinical settings.