Artificial Neural Network: A New Approach for Prediction of Body Fat Percentage Using Anthropometry Data in Adult Females

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Sugandha Mehandiratta, Priyanka Singhal, A. K. Shukla, Rita Singh Raghuvanshi


Assessing body fat using anthropometric data would be useful in predicting chronic diseases. Accurate use of proper statistical models in analysing body composition data is of prime importance. This study was undertaken to assess body composition of diseased and non-diseased women using body composition analyser thereafter using data for development of statistical model. The objective was to find relationship of various anthropometric parameters with Percent Body Fat (BF%) and to develop various prediction models for estimating BF on the basis of anthropometric data. BF% was predicted using Linear Regression (LR), Multiple Linear Regression (MLR), Non-Linear Regression (NLR) and Artificial Neural Network (ANN) models. The predictors used in the study were age (yrs.), height (cm), weight (kg), Body Mass Index (BMI) (Kg/m2) and Waist Circumference (WC) (cm). Data utilized for the study was related to 860 adult females aged 18-60 years out of which 700 were non-diseased and 160 were diseased (diabetic and hypertensive). Out of various models developed using LR, MLR, NLR for Non-Diseased group, three predictors viz. age, BMI and WC were found to be appropriate for estimating BF%. However, the best prediction of BF% was achieved using ANN model taking age, height, weight and WC as predictors (R2 = 0.787). ANN technique was found as the most suitable technique for developing prediction models for estimation of BF% in non-diseased group. However, in diseased group ANN model could not predict BF% more precisely, may be due to some other factors affecting the body composition of females of diseased group.

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How to Cite
, S. M. P. S. A. K. S. R. S. R. “Artificial Neural Network: A New Approach for Prediction of Body Fat Percentage Using Anthropometry Data in Adult Females”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 6, no. 2, Feb. 2018, pp. 117-25, doi:10.17762/ijritcc.v6i2.1433.