Comparison of Machine Learning Methods for Calories Burn Prediction

Authors

• Alfred Tan Jing Sheng, Multimedia University, Malaysia
• Zarina Che Embi, Multimedia University, Malaysia
• Noramiza Hashim, Multimedia University, Malaysia

Corresponding Author ORCID Identifer

Zarina Che Embi https://orcid.org/0000-0001-9378-7380

Section

Regular Issue

Abstract

This paper focuses on the prediction of calories burned during exercise using machine learning techniques. Due to a growing number of obesity and overweight people, a healthy lifestyle must be adopted and maintained. This study explores and compares several machine learning regression models namely LightGBM, XGBoost, Random Forest, Ridge, Linear, Lasso, and Logistic to assess their calories burned prediction performance that can be used in systems such as fitness recommender systems supporting a healthy lifestyle. Our findings show that the LightGBM for predicting calorie burn has a good accuracy of 1.27 mean absolute error, giving users reliable recommendations. The proposed system has a good potential in assisting users in reaching their fitness objectives by offering precise and tailored advice.

Cite This Article

Jing Sheng, A. T., Che Embi, Z., & Hashim, N. (2024). Comparison of Machine Learning Methods for Calories Burn Prediction. Journal of Informatics and Web Engineering, 3(1), 182–191. https://doi.org/10.33093/jiwe.2024.3.1.12

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