Synthetic Diabetes Datasets

This page demonstrates the structure of synthetic datasets available in this category, including variable schema, preview data, and reproducibility using the Syntherx SDK.

Example Dataset

Diabetes Hba1c Trial

Variable schema and example preview from the blueprint definition.

Variable Schema

Column NameTypeDescription
patient_idstringUnique synthetic patient identifier
agenumberSynthetic patient age
sexstringSynthetic patient sex
treatment_groupstringTreatment or control group
baseline_measurenumberBaseline HbA1c measure
outcome_measurenumberOutcome HbA1c measure

Data Preview

First 9 rows (preview only)

patient_idagesextreatment_groupbaseline_measureoutcome_measure
P00000171Femaletreatment8.36.8
P00000243Malecontrol97.1
P00000359Femaletreatment8.87.5
P00000467Malecontrol9.17.5
P00000544Malecontrol8.97
P00000665Maletreatment7.96.9
P00000751Femaletreatment9.68.3
P00000853Malecontrol8.78
P00000951Femaletreatment8.67.4

Includes CSV, JSON, and Parquet — ready for ML pipelines

Reproduce This Dataset

Recreate this dataset in Python (Jupyter, Kaggle, or Google Colab) using the Syntherx SDK

# Install Syntherx SDK
pip install syntherx

from syntherx import generate_dataset

df = generate_dataset(
    blueprint="diabetes_hba1c_trial",
    rows=5000
)

df.to_csv("diabetes_hba1c_trial.csv")

Use Cases

  • Diabetes and glycemic outcomes modeling
  • Glucose and treatment response analysis
  • Risk stratification for diabetes populations

Privacy-Safe Synthetic Dataset

  • Contains no real patient data
  • Generated using statistical simulation
  • Designed for machine learning research

Purchase Dataset

Research Dataset — $99

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Includes CSV, JSON, and Parquet — ready for ML pipelines

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