Cloud Based Network Analysis Model for Predicting Disease Diet Associations

Abstract

Predictive analytics in healthcare is an integration of computational technologies and healthcare domain for retrieval, storage and analysis of medical data. With the immense progress in computational techniques and technologies, healthcare domain has witnessed unparalleled achievements since the last decade. Comprehending the relationship between health and diet is another such area which presents numerous opportunities for predictive healthcare. Disease-diet associations pose an arduous problem in computational domain because of the evident complex interdependencies. The intertwined relations among disease, diet and their subtypes along with the varying nature of their associations (harmful or helpful) adds to the complexity. Thus, the associations need to be explored with a close integration of significant computational techologies. Predictive analysis of such associations would be an aid for healthcare professionals to foresee the risk of occurrence or progression of a disease on the basis of diet and thus make informed decisions. This work aims to efficiently and effectively predict unknown disease-diet associations using integrated computational technologies. To achieve this, initially, a review of the work done in exploring the relation of disease and diet has been undertaken. It is evident from the review that several studies aim to explore the associations, but they have been designed for a specific disease and diet combination. It is also realized that while some disease-diet associations are well established since ages, there are others which have found acknowledgement only in literature. This presents an opportunity for bringing together such studies and exploiting them on a large scale. Further, a survey of the existing services and techniques designed for understanding relation of disease and other factors like drugs, symptoms etc. has been done. It highlights a plethoric use of an upcoming technology Network Analysis for representing and analyzing complex relationships.