Prediction of Obesity Categories Based on Physical Activity Using Machine Learning Algorithms

Muhammad Iqbal; Lisnawanty L; Weiskhy Steven Dharmawan; Rendi Septian

doi:10.47709/cnahpc.v6i3.4053

Authors

Muhammad Iqbal Universitas Bina Sarana Informatika
Lisnawanty L Universitas Bina Sarana Informatika
Weiskhy Steven Dharmawan Universitas Bina Sarana Informatika
Rendi Septian Universitas Nusa Mandiri

DOI:

10.47709/cnahpc.v6i3.4053

Keywords:

Data Mining, Machine Learning, Obesity, Prediction, XGBoost

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Abstract

Obesity is a global health issue with rising prevalence, marked by excessive fat accumulation that poses health risks. Contributing factors include poor eating habits, lack of physical activity, and genetics, which elevate the risk of chronic diseases like type 2 diabetes, heart disease, stroke, and cancer. This study examines an obesity dataset with seven variables: Age, Gender, Height, Weight, BMI, Physical Activity Level, and Obesity Category. The analysis reveals strong correlations between Body Weight, BMI, and the Obesity Category, while Body Height shows a moderate negative correlation. Various machine learning algorithms were tested, including XGBoost, AdaBoost, Gradient Boosting, and Extra Trees Classification. XGBoost emerged as the top performer, achieving the highest accuracy (0.9961) and an almost perfect AUC (0.9992), making it highly effective for obesity prediction. The study's significance lies in its ability to elucidate the key factors contributing to obesity and their interactions. By recognizing the strong links between Body Weight, BMI, and Obesity Category, healthcare professionals can craft more targeted interventions. Furthermore, the successful application of advanced machine learning algorithms underscores the potential for technology to enhance predictive accuracy and support healthcare decision-making. The findings highlight XGBoost's superior performance, demonstrating its value in predicting obesity and aiding in early diagnosis and prevention strategies. This research emphasizes the critical role of data and technology in tackling obesity and improving public health outcomes.

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