Estimation of transition probabilities for diabetic patients using hidden Markov model

Manoj Kumar Varshney (), Ankita Sharma (), Komal Goel (), Vajala Ravi () and Gurprit Grover ()
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Manoj Kumar Varshney: University of Delhi
Ankita Sharma: University of Delhi
Komal Goel: University of Delhi
Vajala Ravi: University of Delhi
Gurprit Grover: University of Delhi

International Journal of System Assurance Engineering and Management, 2020, vol. 11, issue 2, No 21, 329-334

Abstract: Abstract Diabetes is a common non-communicable disease affecting substantial proportion of adult population. This is true, especially in developing countries like India thereby posing a huge economic burden not only on the patient’s family but also on the nation as a whole. In this paper, we have employed a hidden Markov model to estimate the transition probabilities between three states of diabetes and applied it to real life data. A total of 184 Type 2 diabetic patients were included in this study. These patients are classified in different states on the basis of their available baseline value of Hemoglobin A1c (HbA1c). A HMM fits well to the data by capturing the misclassified states, and shows that the patients who had HbA1c ≥ 6.5% have minimum chance of recovery and substantially higher risk of complications. All the statistical analysis has been performed using the “Hidden Markov” package in R software.

Keywords: Diabetes; HbA1c; Hidden Markov model (HMM); Emission distribution; Goodness of fit (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s13198-020-00950-7

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