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
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.
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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.
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Last pushed
Jun 01, 2025
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