ML-powered NCD risk intelligence across every Indian district. Validated across time — trained on 2015–16 data, predicted 2019–21 outcomes with 63% explained variance. Built for insurers, pharma, and public health teams.
Most health data products describe the present. Districtmaps.ai predicted the future — validated independently across districts, time, and conditions.
Every prediction is explainable. SHAP analysis reveals the exact contribution of each feature — so technical buyers can audit the model, not just trust it.
Obesity and tobacco use are the strongest direct causes. Districts where women are overweight and men smoke heavily show consistently high diabetes prevalence — regardless of geography.
Education, healthcare access, and insurance coverage predict diabetes through the development transition pathway — wealthier districts adopt urban diets and sedentary lifestyles before health infrastructure catches up.
Children's overweight status, teen pregnancy rates, and childhood anaemia reveal the intergenerational transmission of metabolic risk — high-risk households today are high-risk districts tomorrow.
Enter any Indian district to retrieve its full NCD risk profile. Try: Mumbai, Chennai, Patna, Hyderabad, Bengaluru.
RESTful JSON API. No SDK required. Returns risk scores for any Indian district. Fuzzy name matching included.
Adjust the preset values, then add up to 3 of your own metrics using whatever column names you actually use. Watch the fuzzy matcher map them — and the prediction sharpen as coverage rises.
The /predict endpoint isn't a lookup. It's a live ML engine that runs on your data, in your naming convention, in under a second.
Send column names exactly as they exist in your system — "loss_ratio", "claims_paid", "obesity_rate". Our fuzzy matcher maps them to the model automatically. No data engineering, no integration sprints.
Every prediction comes with a match report — showing which of your columns drove the score, what confidence they matched at, and which gaps were filled with national medians. Auditable by your risk team.
The more columns you send, the sharper the prediction. With 5 features you get a directional signal. With 20+ you get a portfolio-grade underwriting score calibrated to your district mix.
Cross your district loss ratios against our NCD risk scores. The model surfaces which districts are structurally underpriced — where the health burden isn't yet reflected in your premium structure but will be in 3–5 years.
Send your sales rep territory boundaries and current Rx volumes. Our model returns the latent NCD burden in each territory — where the undiagnosed patient pool is largest and growing fastest.
Before opening a new facility, score every shortlisted district against projected NCD patient volumes. Our temporal validation — 63% explained variance 4 years forward — makes it a credible input to your capital allocation model.
Send your intervention budget and district population data. The API returns a ranked priority list — the districts where NCD burden is highest relative to existing healthcare infrastructure investment.
API access is currently invite-only while we calibrate for enterprise workloads. Tell us what you're working on — we respond within 48 hours.