Prediction of Total Body Water using Scaled Conjugate Gradient Artificial Neural Network

Abstract

The study aims to design an intelligent total body water measuring device which will help to determine the total body water level or percentage of an individual using ultrasonic sensor, load cell and bioelectric impedance analysis (BIA). The system utilized the Scaled Conjugate Gradient Artificial Neural Network (ANN) as the machine learning algorithm. The system used the dataset splitting of 70%-15%15% for training, validation and testing. Different hidden neurons were used and compared during neural network training and found out that using 10 neurons will provide the lowest mean square error (MSE) with best value of Pearson’s correlation (R). Based on the results, using 10 neurons, Scaled Conjugate Gradient algorithm has better performance as compared to Levenberg-Marquardt algorithm with MSE equal to 0.180033, 0.118954, 0.529157 while the R value is equal to 0.997887, 0.997488, 0.99644 for training, validation and testing.