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.
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Initial state distribution
- (S1, S2…SN):
Sequence of hidden state
Sequence of observed state
Transition probability matrix
Emission probability matrix
- aij :
The probability of being in hidden state Sj at time (t + 1) given that the patient was in hidden state Si at time t
- bjk :
The probability of being in observed state k at time t given that the patient was in hidden state Sj at time t
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Varshney, M.K., Sharma, A., Goel, K. et al. Estimation of transition probabilities for diabetic patients using hidden Markov model. Int J Syst Assur Eng Manag 11, 329–334 (2020). https://doi.org/10.1007/s13198-020-00950-7
- Hidden Markov model (HMM)
- Emission distribution
- Goodness of fit