MACHINE LEARNING APPROACH FOR PREDICTING BODY FAT
DOI:
https://doi.org/10.61841/zqyg3b32Keywords:
Body Fat,, Multiple Linear Regression,, Support Vector Regression,, Machine Learning.Abstract
A human body needs a certain amount of fat to function properly. Fat controls the body temperature protects the organs, and store energy for body functioning. For the human body, it is important to assess the current status of the body fat in order to make correct decisions to improve health. Recently, the Machine Learning approach has empowered strong and accurate predictions on many healthcare applications. Regression analysis is one such supervised machine learning approaches which is used to analyze the significant factors which will affect the body depending on the fat content. In this paper, regression analysis techniques such as Multiple Linear Regression (MLR), and Support Vector Regression (SVR) are applied and analyzed to predict the body fat. The performance of the algorithms has been evaluated based on regression models validation metrics such as Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE).
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