Authors:
Giridhar Krishnan
and
Waqar Haque
Affiliation:
Department of Computer Science, University of Northern British Columbia, Prince George, Canada
Keyword(s):
Predictive Modeling, Health Informatics, Diabetes, Comorbidities, Health Care Systems.
Abstract:
Diabetes is one of the most common chronic diseases in the world with patients being more susceptible to develop additional comorbidities over time. In this research, we have used clinical data collected over six years to perform predictive and visual analytics which enables healthcare professionals gain valuable insight into early identification of the risk of developing comorbidities thereby resulting in effective diabetes management and reduced burden on healthcare system. We first present predictive models developed to forecast the likelihood of one of the three common comorbidities for diabetic patients – Benign Hypertension, Congestive Heart Failure, and Acute Renal Failure. The models use advanced data mining algorithms such as Logistic Regression, Neural Network, CHAID, Bayesian Network, Random Forest and Ensemble. Results from these models are incorporated into an interactive assessment tool that can take user input and predict the likelihood of developing one of these comor
bidities. In addition, an interactive diabetes dashboard presents aggregated data using visually appealing charts, graphs, and tables. The dashboard also provides drilldown capabilities to allow navigation at finer granularities of various metrics.
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