A second opinion
on the heart, in
two hundred milliseconds.
CardioSense fuses eleven clinical signals through a gradient-boosted classifier trained on roughly seventy-thousand cardiovascular records — returning a calibrated probability of disease and a transparent, factor-by-factor breakdown.
sedentary, hypertensive.
Quantified risk, backed by data.
Every diagnosis returns three artefacts — a number, a story, and a record. Together they support a clinical conversation rather than replace one.
Calibrated probability
A continuous probability score — not a binary verdict — so you can triage rather than guess.
Vitals breakdown
Each input scored against clinical thresholds: BP, BMI, cholesterol, glucose, lifestyle.
Private patient log
Every diagnosis is timestamped and saved to your account — searchable and exportable.
Boosted trees on tabular reality.
CardioSense is built on XGBoost — a gradient-boosted tree ensemble that excels at the structured, mixed-type tabular data that defines clinical screening. The underlying corpus combines the public Cardiovascular Disease Dataset with Framingham-derived features.
Inference is delegated to a dedicated Flask service on Railway, called from a type-safe TanStack server function. The score is returned, displayed, and persisted to a row-level-secured patient record.
Full methodologyEleven inputs. One readout.
⚕ For screening and educational use only — never replaces clinical judgement