Nutritional biomarkers and machine learning for personalized nutrition applications and health optimization

Abstract

The doctrine of the “one size fits all” approach has been overcome in the field of disease diagnosis and patient management and has been replaced by a more per patient approach known as “personalized medicine”. Biomarkers are the key variables in the research and development of new methods of training prognostic models and neural networks in the scientific field of machine learning and artificial intelligence [1] [2]. Important biomarkers related to metabolism are the metabolites. Metabolomics refers to the systematic study of unique chemical fingerprints that are left behind by specific cellular processes. The metabolic profile can provide a snapshot of cell physiology and, by extension, metabolomics provide a direct “functional reading of the physiological state” of an organism. The goal of this paper is to employ current machine learning methodologies, specifically neural networks, to formulate a general evaluation chart of the nutritional biomarkers, to investigate how to best predict body mass index and to discover dietary patterns.