Enhanced Diet Recommendations for Diabetes and Nephrolith Conditions using Transformer Networks

An upsurge of chronic illnesses including diabetes, nephrolith etc… has created the need for individualized nutrition intervention. The existing traditional dietary advice interventions systems are not personalized and cannot classify people based on their genetic make-up, their metabolic signature, and their lifestyle with regards to those conditions, which makes their management less than optimal. The objective is to develop a dietary recommendation system using transformer network for effective management of diabetes and nephrolith conditions. the proposed methodology articulates a transformer based dietary recommendation system that will help the patients, especially the ones with chronic illnesses, come up with personalized nutrition plans to meet their dietary requirements. The system utilizes transformer networks, for predicting the diet based on the disease selected. The use of the proposed model will be of more benefit since its results depict better accurateness (more than 90<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>) and patient compliance than other conventional machine learning models. The system also reveals potential in boosting chronic disease management by practicing individualized dietary approaches. Future work emerging from this study includes real-time data assimilation and extending the model for managing multiple diseases.