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Publication Type : Conference Paper
Publisher : IEEE
Source : Proceedings of the 2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023, 2023
Url : https://ieeexplore.ieee.org/document/10200647
Campus : Bengaluru
School : School of Engineering
Department : Electrical and Electronics
Year : 2023
Abstract : Predicting body fat percentage is essential for addressing the obesity problem. This paper compares the performance of several machine learning models based on Regression, to predict the body fat percentage. Using a dataset of 252 participants with information on age, weight, height, and fat percentage, the models were assessed based on multiple performance criteria, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Squared Error(MSE). The results demonstrates that Random Forest Regressor surpass other models with a lower RMSE of 0.276. These findings suggest that machine learning models can be a valuable tool for precise BFP, the use of machine learning provides a faster and more precise method for predicting body fat percentage. Overall, the study’s results suggest that machine learning models can be valuable tool for accurate body fat percentage prediction.
Cite this Research Publication : Mahesh, N., Pati, P.B., Deepa, K., Yanan, S., "Body Fat Prediction using Various Regression Techniques", Proceedings of the 2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023, 2023