Diabetes Risk Analytics
Estimate Type II diabetes risk from clinical biomarkers. Enter patient data and get a risk score with clear review bands.
What this solution does
A peer-reviewed model that estimates Type II diabetes risk from biomarker and body measurement data, giving teams a clear risk score for prevention planning.
Chronic-disease data is often incomplete and hard to compare across programmes. This tool helps teams spot higher-risk groups and plan reporting priorities from the data they have.
Outputs are model estimates for analytics and research review, not a clinical diagnosis or treatment recommendation. We document the inputs, assumptions, and limits behind every score.
Model basis: Ojurongbe, T. A., Afolabi, H. A., Oyekale, A., Bashiru, K. A., Ayelagbe, O., Ojurongbe, O., ... & Adegoke, N. A. (2024). Predictive model for early detection of type 2 diabetes using patients' clinical symptoms, demographic features, and knowledge of diabetes. Health Science Reports, 7(1), e1834.
Who it serves and where it applies
- Programme managers and institutional analytics teams
- Researchers studying cardiometabolic risk
- NGOs and community health initiatives
- Public-health and prevention-focused partners
- Cohort stratification (grouping people by risk level) for prevention programmes
- Risk reporting for dashboards and reviews
- Research prototyping with structured chronic-disease data
- Cross-condition analytics planning