You are now in the main content area

Faculty & Research

Our school brings together scholars and practitioners from a range of disciplines—engineering and science, business, information systems, health, social sciences and the humanities—to study the issues critical to success in the new digital economy.

Their past and current involvement in the industry means that they bring fresh, relevant ideas to the classroom. While some are involved in groundbreaking research, others are frequently asked to speak at professional and academic conferences. It's more than just their accomplishments and research that make them valuable to the program - they're also experienced instructors and expert communicators.

Faculty Research Spotlight

January 2, 2018

Dr. Aziz Guergachi's manuscript entitled "A Systematic Machine Learning Based Approach for the Diagnosis of Non-Alcoholic Fatty Liver Disease Risk and Progression" has been accepted for publication in Scientific Reports, external link.

Abstract

Prevention and diagnosis of NAFLD is an ongoing area of interest in the healthcare community. Screening is complicated by the fact that the accuracy of noninvasive testing lacks specificity and sensitivity to make and stage the diagnosis. Currently no non-invasive ATP III criteria based prediction method is available to diagnose NAFLD risk. Firstly, the objective of this research is to develop machine learning based method in order to identify individuals at an increased risk of developing NAFLD using risk factors of ATP III clinical criteria updated in 2005 for Metabolic Syndrome (MetS). Secondly, to validate the relative ability of quantitative score defined by Italian Association for the Study of the Liver (IASF) and guideline explicitly defined for the Canadian population based on triglyceride thresholds to predict NAFLD risk. We proposed a Decision Tree based method to evaluate the risk of developing NAFLD and its progression in the Canadian population, using Electronic Medical Records (EMRs) by exploring novel risk factors for NAFLD. Our results show proposed method could potentially help physicians make more informed choices about their management of patients with NAFLD. Employing the proposed application in ordinary medical checkup is expected to lessen healthcare expenditures compared with administering additional complicated test.