Development of a new software for resting metabolic rate prediction using machine learning methods

Resting metabolic rate(RMR) indicates the number of calories that are needed to carry out basic functions in mammals like blood circulation, brain functions, breathing, fuel ventilation, temperature regulation, etc. at complete rest. Accurate prediction of RMR plays a critical role to detect an individual's establish daily calorie needs, risk of heart disease and stroke, hypertension, diabetes, and internal age so nutritionists calculate RMR of patients within the specified period and able to prepare diet lists for them to help their wellness goals. In this thesis, it was objected to develop a novel web-based application that can predict the individual's RMR using different machine learning methods. The application has been developed using DTREG predictive modeling software library, Visual Studio, and C# programming language. Three different machine learning methods which are General Regression Neural Network (GRNN), Multi-Layer Perceptron (MLP), Support Vector Machines (SVM) have been integrated into the software. Different prediction models have been assessed according to their Root Mean Square Error (RMSE) metrics. As a result, it has been proven that this software can be used for RMR prediction, producing acceptable error rates under certain circumstances.