Harnessing AI for Health: Optimized Neural Network Models for Resting Metabolic Rate Prediction

Abstract

Resting Metabolic Rate (RMR) is a pivotal metric reflecting the caloric expenditure necessary for fundamental physiological functions during periods of rest, encompassing vital processes such as blood circulation, brain activity, respiration, fuel metabolism, and thermal regulation. The accurate estimation of RMR holds paramount importance in delineating an individual’s daily energy requirements and assessing their susceptibility to various health conditions, including cardiovascular diseases, hypertension, and diabetes. Additionally, RMR serves as a significant indicator of biological age, aiding in the formulation of personalized dietary plans by nutritionists to assist individuals in achieving their health and wellness objectives. This study aims to develop new models for predicting RMR using General Regression Neural Network (GRNN). Additionally, prediction models based on Multi-Layer Perceptron (MLP) will be created for comparison purposes. For model optimization, we developed an innovative web-based software using the DTREG predictive modeling software library. The implementation of this application was executed using Visual Studio and the C# programming language. To ensure the robustness of the prediction models, two distinct self-created datasets comprising data from female and male patients were employed. A total of 260 female patients and 150 male patients were included in the datasets. Separate GRNN-based and MLP-based models were developed for each dataset, leveraging various variables obtained from Tanita, a smart scale capable of comprehensive body scanning. These variables encompass personal information, body analysis, reference analysis, and segmental body composition analysis, providing detailed insights into impedance, fat percentage, fat mass, non-fat mass, muscle mass, and other metrics. The variables derived from Tanita measurements served as input variables for machine learning model creation, while RMR values obtained using FitMate served as output variables. Evaluation of these prediction models was based on Correlation Coefficient (R), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics to assess their performance, with the outcomes substantiating the efficacy of the proposed models in RMR prediction. The GRNN-based models performed better than the ones built by MLP, demonstrating its feasibility as a tool for predicting RMR accurately. The results have also been compared with up-to-date prediction equations from related literature, and our optimized models perform significantly better than those. The proposed best performing GRNN model achieved an R value of 0.85, RMSE of 134.91 kcal/day, and MAPE of 10.20%, significantly outperforming traditional predictive equations such as the Harris-Benedict and Mifflin-St Jeor equations, which showed RMSE values above 400 kcal/day and MAPE values exceeding 12.5%.

Keywords

• Resting metabolic rate
• general regression neural network
• multilayer perceptron
• prediction
• Resting metabolic rate
• general regression neural network