Comparison of Machine Learning Methods for Calories Burn Prediction


Tan, Jing Sheng Alfred and Che Embi, Zarina and Hashim, Noramiza (2024) Comparison of Machine Learning Methods for Calories Burn Prediction. Journal of Informatics and Web Engineering, 3 (1). pp. 182-191. ISSN 2821-370X

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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.

Item Type: Article
Uncontrolled Keywords: Exercise, Machine learning, Prediction, Calories burn, Recommender system
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines
Divisions: Faculty of Computing and Informatics (FCI)
Date Deposited: 02 Apr 2024 07:18
Last Modified: 02 Apr 2024 07:18


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