Dark Mode
Glucose and Insulin Levels Dataset
Patient Health Records & Digital Health
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset provides key insights into diabetes, a chronic disease characterised by high blood glucose levels. It encompasses various physiological measurements and health indicators, offering a foundation for understanding the disease's manifestation and progression. The data supports analysis of how the body processes blood sugar, impacted by insulin production or resistance, and includes symptoms like feeling tired, hungry, excessively thirsty, and passing more urine than usual.
Columns
- Pregnancies: Records the number of times a person has been pregnant.
- Glucose: Indicates the level of sugar in the blood.
- BloodPressure: Measures the stability of blood pressure levels.
- SkinThickness: Provides data related to body skin thickness.
- Insulin: Reflects the body's insulin levels, indicating need or not.
- BMI: Results from body mass index tests.
- DiabetesPedigreeFunction: Offers additional information related to diabetes pedigree.
- Age: Categorises individuals as adult or older based on their age.
- Outcome: The result, typically indicating the presence or absence of diabetes.
Distribution
The dataset is provided as a CSV file, named
diabetes.csv, with a size of 23.87 kB. It contains 9 columns and 768 records/rows, with all data entries validated and no missing values across any of the attributes.Usage
This dataset is ideally suited for data analysis, predictive modelling, and logistic regression tasks related to diabetes. It can be utilised to explore the correlation between various health indicators and diabetes onset, aiding in the development of risk assessment models and public health interventions. Python environments like IPython are suitable for its analysis.
Coverage
The dataset includes various physiological and health-related measurements for 768 individuals. While specific geographic or time range details are not provided, the scope covers a range of ages, from 21 to 81 years, and includes details such as pregnancy counts, offering demographic insights into the study population.
License
CC0: Public Domain
Who Can Use It
- Medical researchers for studying diabetes trends and risk factors.
- Data scientists for building machine learning models to predict diabetes.
- Public health analysts for understanding disease prevalence and informing health policies.
- Students and educators for learning about chronic disease data analysis.
Dataset Name Suggestions
- Diabetes Health Predictor Dataset
- Diabetic Indicators Data
- Glucose and Insulin Levels Dataset
- Chronic Disease Prediction Data
- Medical Diabetes Study
Attributes
Original Data Source: Glucose and Insulin Levels Dataset
QUALITY