Classifying recorded obesity levels

Version: 2026-09-21

The UCI dataset contains 2,111 records associated with Colombia, Peru and Mexico. The repository states that 77% were generated synthetically and 23% were collected through a web platform. Height and weight are inputs, so this is classification of recorded status rather than prediction of a future health outcome.

Synthetic records and related anthropometric predictors limit interpretation of the internal split. Compare with a transparent BMI-based classification, separate original from synthetic subjects, and obtain external validation. Scores are not calibrated probabilities and must not guide individual diagnosis or treatment; clinician or specialist review is required for medical use.

Open the .nd project in Neural Designer. Its embedded data, parameters, saved sample roles and task report are preserved. If the original CSV path is unavailable, select the CSV included here. The source CSV may include unused columns; the embedded project defines the exact modelling schema.

The charts/ directory contains standalone HTML exported with the application chart builder. The schema and test-metrics files identify the exact inputs, outputs, evidence and source hash.

Dataset source: https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition
Retain the source attribution and its applicable dataset terms.

The original HTML expression is included unchanged. native-model.js contains the same mathematical functions extracted from it.
