Artificial Neural Network Algorithm in Nutritional Assessment: Implication for Machine Learning Prediction in Nutritional Assessments in Strict Veganism

Abstract

A considerable number of published research has indicated that evaluating the success of weightloss therapy involves proper dietary examination. On the other hand, the bulk of dietary evaluation methods currently in use have favored manual memory recall. In the  current study, we used an artificial neural network (ANN) machine learning algorithm to construct an artificial intelligencebased  nutritional assessment system. The algorithm used information from a user's regular meals as well as their preexisting health indicators  to formulate a machine based nutritional assessment requirement. ANN-based nutritional evaluation approaches will make it possible to  assess eating habits, recommend daily meals, and improve general health. In particular, we develop a machine learning technique to  identify multiple food items by classifying them using an ANN machine algorithm and identifying suitable nutritional assessments using  anthropometric, biochemical, clinical, and dietary (ABCD) data. Using an ANN machine learning model, the artificial intelligence system  initially creates a number of proposals from the input. Next, using information from the unique ABCD nutritional evaluation, it creates  feature maps for each proposal and used the ANN machine learning algorithm to classify diet interval and its composition. Lastly, using the UK-based Dietary Reference Values (DRVs) ranges as a basis, we examined the user's nutritional evaluation obtained from the  system. The results of the experiment shown that our system can reliably identify food items and quickly provide nutritional assessment  reports, which will give users a clear understanding of practical and healthy eating recommendations in a strictly vegan diet.