Anamicca23/Muli-Class-Obesity-Risk-Level-Prediction-Project-using-ML

Advancing Healthcare with 91% Accurate Prediction of Obesity Risk Levels Using XGBoost ,LightGBMand CatBoostClassifier Model

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Experimental

This project helps healthcare professionals and public health organizations predict an individual's obesity risk level. By inputting demographic details, lifestyle habits, dietary patterns, and physical activity levels, it outputs a classification of their obesity risk. This is useful for doctors, nutritionists, or health educators looking to identify at-risk populations and provide targeted interventions.

No commits in the last 6 months.

Use this if you need to quickly assess an individual's likelihood of obesity based on their personal health and lifestyle data.

Not ideal if you require real-time patient monitoring or a diagnostic tool that replaces clinical assessment.

public-health preventive-medicine nutrition-science health-risk-assessment lifestyle-management
No License Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 4 / 25
Maturity 8 / 25
Community 13 / 25

How are scores calculated?

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Last pushed

Jun 01, 2025

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