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Artificial intelligence, chronic diseases, research, food-health


Currently hundreds of studies in the literature have shown the link between food and reducing the risk of chronic diseases. This study investigates the use of natural language processing and artificial intelligence techniques in developing a classifier that is able to identify, extract and analyze food-health articles automatically. In particular, this research focusses on automatic identification of health articles pertinent to roles of food in lowering the risk of cardiovascular disease, type-2 diabetes and cancer as these three chronic diseases account for 60% of deaths (WHO, 2015). Three hundred food-health articles on that topic were analyzed to help identify a unique key (Identifier) for each set of publications. These keys were employed to construct a classifier that is capable of performing online search for identifying and extracting scientific articles in request. The classifier showed promising results to perform automatic analysis of food-health articles which in turn would help food professionals and researchers to carry out efficient literature search and analysis in a timelier fashion.


A Thesis submitted to the Faculty of Applied Science and Technology, School of Applied Computing in partial fulfillment of the requirements for the degree of Honours Bachelor of Computer Science (Mobile Computing) Sheridan College, Institute of Technology and Advanced Learning


Faculty of Applied Science & Technology (FAST)

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Original Publication Citation

Ross, M. (2018). Exploring identifiers of research articles related to food and disease using artificial intelligence (Unpublished thesis). Sheridan College, Ontario, Canada.