Original Research ARTICLE
A hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression
- 1University of Engineering and Technology, Lahore, Pakistan
- 2University of Engineering & Technology, Pakistan
- 3University of Toronto, Canada
- 4Ryerson University, Canada
Type 2 Diabetes Mellitus (T2DM) is a chronic, progressive metabolic disorder characterized by hyperglycemia resulting from abnormalities in insulin secretion, insulin action, or both. It is associated with an increased risk of developing vascular complication of micro as well as macro nature. Because of its inconspicuous and heterogeneous character, the management of T2DM is very complex. Modeling physiological processes over time demonstrating the patient’s evolving health condition is imperative to comprehending the patient’s current status of health, projecting its likely dynamics and assessing the requisite care and treatment measures in future. Hidden Markov Model (HMM) is an attractive tool for such prognostic modeling. However, the nature of the clinical setting, together with the format of the Electronic Medical Records (EMRs) data, in particular the sparse and irregularly sampled clinical data which is well understood to present significant challenges, has confounded standard HMM. In this paper, we propose an approximation technique based on Newton’s divided difference method as a component with HMM to determine the risk of developing diabetes in an individual over different time horizons using irregular and sparsely sampled EMRs data. The proposed method is capable of exploiting available sequences of clinical measurements obtained from a longitudinal sample of patients for effective imputation and improved prediction performance. Furthermore, results demonstrated that the discrimination capability of our proposed method, in prognosticating diabetes risk, is superior to the standard HMM.
Keywords: type 2 diabetes mellitus, machine learning, Hidden markov model, Prognostic modeling, risk prediction
Received: 08 Jul 2019;
Accepted: 09 Oct 2019.
Copyright: © 2019 Perveen, Shahbaz, Keshavjee and Guergachi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mx. Sajida Perveen, University of Engineering and Technology, Lahore, Lahore, 54590, Punjab, Pakistan, Sajida.firstname.lastname@example.org