Classification of Obesity Risk Levels in Adolescents Based on Lifestyle using the Random Forest Algorithm

Giatika Chrishnawati; Agung Nawawi; Aisyah Adzahra; Mumammad Rafi Shafa

doi:10.59934/jaiea.v5i2.2138

Authors

Giatika Chrishnawati Universitas Bina Sarana Informatika
Agung Nawawi Universitas Bina Sarana Informatika
Aisyah Adzahra Universitas Bina Sarana Informatika
Mumammad Rafi Shafa Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.59934/jaiea.v5i2.2138

Keywords:

Adolescent Obesity; Lifestyle Patterns; Machine Learning; Obesity Risk; Random Forest.

Abstract

Obesity in adolescents is a health problem influenced by diet, physical activity, and sedentary habits. This study aims at the classification of obesity risk in adolescents using a classification approach with the Random Forest algorithm based on the Obesity Levels and Lifestyle Dataset obtained from the Kaggle platform. The research stages include data preprocessing, dividing training and test data, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The test results showed an accuracy value of 93% with an average F1-score value of 0.93, indicating that the model has consistent performance in distinguishing various levels of obesity risk. Feature importance analysis shows that in addition to physical factors such as body weight, lifestyle factors such as physical activity and food consumption patterns also contribute significantly to the classification process. These findings confirm that the Random Forest algorithm is effective as a tool for early classification of obesity risk based on lifestyle, and can be the basis for developing a preventive adolescent health monitoring system.

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