Predictive modeling for early detection and prediction of diabetes and cardiovascular diseases using big data and machine learning.

Objective: This scoping review aimed to explore how big data and machine learning techniques are currently applied to develop predictive models for the early identification and prediction of diabetes and cardiovascular diseases, while identifying methodological strengths, implementation gaps, and ethical considerations. Methods: Following PRISMA-ScR guidelines and the Arksey and O'Malley framework, a comprehensive literature search was conducted across five major databases, covering studies published from 2013 to 2025. Studies were included if they used ML with big data sources to predict diabetes or cardiovascular diseases. Data were extracted, thematically synthesized, and quality appraised using a modified Mixed Methods Appraisal Tool (MMAT). Results: Out of the screened studies, 66 studies were included in the final synthesis. Most employed supervised machine learning techniques such as decision trees and ensemble models, drawing on electronic health records, wearable sensors, and multi modal datasets. Deep learning approaches, while less common, showed promise in handling unstructured data. Key challenges included data heterogeneity, limited external validation, and underrepresentation of diverse populations. Ethical issues like algorithmic bias and lack of model interpretability were noted. Real-world implementation remained sparse, with few models integrated into clinical workflows. Conclusion: Predictive modeling using big data and ML holds significant promise for early disease detection, yet translation into practice is hindered by methodological, infrastructural, and ethical challenges. Future efforts must prioritize transparency, inclusivity, and interdisciplinary collaboration to ensure responsible deployment in real-world healthcare settings.