Nutritional biomarkers and machine learning for personalized health optimization
What is it about?
Biomarkers are key variables in the research and development of new methods for training prognostic and classification models based on advances in artificial intelligence. Biomarkers contribute significantly towards “personalized medicine”. Specifically, with the deployment of deep neural networks, a link between two very important metabolic routes has been explored. The pathogenic route is investigated through a classification based on the Body Mass Index (BMI), via identifying patterns in the biochemical profile compiled via a simple blood test. The dietary route is looked into in a similar way via linking the diet intake with the biochemical profile elements that seem to be more influential in defining our samples weight class. By separating the weight classes in different groups and by balancing the data, an improvement in accuracy was achieved. Through the deployment of different combinations for data features, the main conclusion was that the quantity of data is the main benefactor in the deployed system predictability. There is extensive bibliography regarding metabolism, dietary profiling, metabolomics and related research that utilizes statistical methods for correlation and causation purposes. Our aim is to design a model that can automate a mundane procedure, to create a fast and easily applicable method for scaled applications with the use of deep neural networks and pattern recognition techniques.
Why is it important?
The doctrine of the “one size fits all” approach in the field of disease diagnosis and patient management is being replaced by a more per patient approach known as “personalized medicine”. In this spirit, biomarkers are key variables in the research and development of new methods for prognostic and classification model training based on advances in the field of artificial intelligence. Metabolomics refers to the systematic study of the unique chemical fingerprints that cellular processes leave behind. The metabolic profile of a person can provide a snapshot of cell physiology and, by extension, metabolomics provide a direct “functional reading of the physiological state” of an organism. Via employing machine learning methodologies, a general evaluation chart of nutritional biomarkers is formulated and an optimised prediction method for body to mass index is investigated with the aim to discover dietary patterns.