metadata
license: unknown
language:
- en
tags:
- medical
Estimation of Obesity Levels Based on Eating Habits and Physical Condition
Overview
This dataset is designed to estimate obesity levels based on several parameters including eating habits, physical condition, and lifestyle. It includes data from diverse individuals across different demographics, offering insights for research in healthcare, nutrition, and public health.
Dataset Characteristics
- Type: Multivariate
- Number of Instances: 2111
- Number of Attributes: 17
- Associated Tasks: Classification
- Missing Values: No
Attributes Information
- Gender: Male/Female
- Age: Numeric, years
- Height: Numeric, meters
- Weight: Numeric, kilograms
- Family History with Overweight: Yes/No
- Frequent Consumption of High Caloric Food: Numeric (0-3)
- Frequent Consumption of Vegetables: Numeric (0-3)
- Main Meals: Numeric (1-3)
- Food Consumption Between Meals: Numeric (0-3)
- Smoking Habit: Yes/No
- Daily Water Consumption: Numeric (1-3)
- Calories Consumption Monitoring: Yes/No
- Physical Activity Frequency: Numeric (0-3)
- Daily Use of Technology Devices: Numeric (0-3)
- Transportation Mode: Walking/Public Transport/Automobile
- Obesity Level: Categorical (Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II, Obesity Type III)
Usage
This dataset is useful for studies related to:
- Analyzing the correlation between eating habits, lifestyle, and obesity.
- Developing machine learning models to predict obesity levels.
- Evaluating public health interventions targeting obesity.
Citation
If you use this dataset in your research, please cite the following:
Adrián Colomer, Jorge Sancho, Alejandra Calafate, Jose-Carlos Cano, and Pietro Manzoni. “Estimation of Obesity Levels Based on Eating Habits and Physical Condition.” UCI Machine Learning Repository.