Behavioural, cognitive, and computational risk factors for type 2 diabetes: A systematic review

Background: Type 2 diabetes mellitus represents a global public health challenge, with rising prevalence driven by complex interactions between lifestyle factors, health literacy, cultural beliefs, and demographic characteristics.Despite extensive research, few studies have systematically examined the interplay between behavioural determinants, cognitive awareness, misconceptions, and computational prediction models within integrated frameworks.Aim: This systematic review synthesises evidence on multidimensional risk factors for type 2 diabetes, examining lifestyle behaviours, health literacy and awareness, cultural misconceptions, family history, and the application of machine learning approaches in risk prediction and behavioural profiling.Method: A systematic search was conducted across major databases including PubMed, Scopus, and Web of Science.Studies published between 2008 and 2025 examining lifestyle factors, health literacy, awareness, misconceptions, family history, and computational approaches related to type 2 diabetes risk were included.Quality assessment was performed, and data were synthesised narratively across five thematic domains.Results: Thirteen studies met inclusion criteria, revealing a fragmented evidence base.While individual domains showed strong associations (e.g., sedentary behaviour with diabetes risk, health literacy with preventive behaviour, and cultural misconceptions with treatment adherence), no study successfully integrated behavioural, cognitive, and computational factors within a single predictive framework.Health literacy and awareness significantly influenced preventive behaviours, while cultural misconceptions impeded effective disease management.Family history emerged as a consistent non-modifiable risk factor.Machine learning models demonstrated high predictive accuracy but often lacked behavioural and cognitive variables, limiting their clinical applicability.Few studies integrated multiple dimensions simultaneously.Conclusion: This review highlights critical gaps in holistic diabetes risk assessment.Future research should develop integrated frameworks combining behavioural profiling, cognitive assessments, and explainable artificial intelligence to enable personalised prevention strategies and improve clinical decision making.