Artificial intelligence-enabled obesity prediction: A systematic review of cohort data analysis

• Published in: International journal of medical informatics (Shannon, Ireland) Vol. 196; p. 105804
• Main Authors: Niakan Kalhori, Sharareh Rostam, Najafi, Farid, Hasannejadasl, Hajar, Heydari, Soroush
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.04.2025
• Subjects: Algorithms; Artificial Intelligence; Cohort; Cohort Studies; Humans; Machine Learning; Obesity; Obesity - diagnosis; Obesity - epidemiology; Prediction; Obesity; Cohort; Artificial intelligence; Prediction
• ISSN: 1386-5056; 1872-8243; 1872-8243
• DOI: 10.1016/j.ijmedinf.2025.105804

•AI techniques hold potential for obesity prediction and mitigating complications caused by obesity.•The analysis of the cohorts in the reviewed studies demonstrates a wide variety in terms of both the number of participants and the duration of these cohorts.•Researchers commonly employ supervised learning methodologies, such as random forest and linear regression models for the purpose of predicting obesity.•Demographic and Biomarkers data are frequently employed for obesity prediction.•The ANN model exhibits superior performance in terms of AUC (Area Under the Curve) and k-means in terms of accuracy. Obesity, now the fifth leading global cause of death, has seen a dramatic rise in prevalence over the last forty years. It significantly increases the risk of diseases such as type 2 diabetes and cardiovascular disease. Early identification of obesity risk allows for preventative actions against obesity-related factors. Despite the existence of AI-based predictive models, developing a comprehensive obesity screening tool requires extensive cohort data. A thorough review of 6,351 articles, focusing on AI predictions for obesity in cohort studies, was conducted up to March 2024 across databases including PubMed, Scopus, and Web of Science. Using the JBI checklist, 10 studies involving 411,580 participants were critically appraised. These cohorts varied in length and size, with half lasting 1–5 years and involving less than 5,000 participants. The data types were categorized into nine groups, with demographic (7 studies) and biomarker data (4 studies) being the most frequently used. Machine learning was predominantly used (95 % of studies), mostly employing supervised learning techniques. Algorithms like random forest (RF) (18 %), linear regression (18 %), and stochastic gradient boosting (GBM) (14 %) were common. Top-performing models were noted for k-means (accuracy of 0.977), artificial neural networks (AUC of 0.99), GBM (specificity of 0.95 and sensitivity of 0.65), RF (RMSE of 0.146), and least absolute shrinkage and selection operator (r-squared of 0.684). Findings indicate that AI algorithms can predict obesity; however, further research is needed to assess their effectiveness in analyzing obesity-related data and examine most advanced AI methods. This review is a valuable resource for dietitians and researchers engaged in developing predictive models and intelligent clinical decision support systems using AI technology.