Diabetes Health Indicators is a comprehensive health and medical analytics dataset designed to support diabetes risk prediction, public health research, and machine learning modeling.

This dataset contains 31 diabetes feature fields, covering four major categories of variables: demographic characteristics, lifestyle, medical history, and clinical indicators. Each record represents an individual's health status profile, integrating demographic attributes, lifestyle habits, family medical history, and physiological measurements, providing a complete data foundation for multidimensional health modeling. The dataset has undergone complete preprocessing, has a standardized structure and balanced distribution, meets clinical validation standards, and can be directly used for scientific research analysis and model training.

Main fields:

• Demographic information, such as age, sex, race, education level, income category, and occupation type, is used to analyze health disparities.
• Lifestyle characteristics, such as smoking, alcohol consumption, diet quality, sleep patterns, and exercise frequency, are used to assess the impact of lifestyle behaviors on diabetes risk.
• Medical history information: family history of genetic predisposition, hypertension, cardiovascular disease, etc., to improve the medical interpretability of the model.
• Clinical measurement indicators such as body mass index (BMI), blood pressure, cholesterol, triglycerides, fasting and postprandial blood glucose, insulin levels, and glycated hemoglobin (HbA1c) are used to quantify physiological status and disease progression.
• Target variables include diabetes diagnosis (whether the patient has the disease) and disease stage (pre-diabetes, type 1, type 2), which can be used for binary or multi-class classification modeling tasks.