Implementation of a Markov Model for the Analysis of Parkinson’s Disease

  • K. M. Mancy
  • G. Suresh
  • C. VijayalakshmiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1045)


In this paper, Parkinson’s disease which is the most common form of neurodegenerative disorder is analyzed. As research advances more than quite a long while, it is apparent that it has turned out to be tricky to follow the growth of Parkinson’s disease. The mathematical model has been implemented for this disease. The progressive nature of this study is focused through Markov chain process. The long-run fraction method is used to calculate the individuals residing in each state. Succeeding, the paper portrayed the traits of finite-state Markov models in nonstop time frequently used to show the course of illness for assessing change rates and probabilities. Our results encourage the statistical approach to analyze the severity of patient health conditions to determine the steady-state distribution of the Markov model. It is expected that the theoretical analysis and experimental results of the study aid to improve hypothetical methods and interventions for the neurodegenerative disorder diseases and to control the risk in future.


Mental health disorders Parkinson’s disease Markov processes TPM Steady-state distribution Neurodegenerative disorder 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Mathematics Division, SASVellore Institute of TechnologyChennaiIndia
  2. 2.Department of MathematicsVV College of EngineeringTirunelveliIndia

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