Hidden Markov Model of Evaluation of Break-Even Point of HIV patients: A Simulation Study

Abstract

Background: The HIV virus carries projection of significant global population with specific estimations of the mathematical results of evolutionary methods which was presented in Tree Hidden Markov model (HMM). Materials and Methods: Hidden Markov models used to model the progression of the disease among HIV infected people. The author predicts a Baum Welch Algorithm method through HMM that can assess an unknown state of transition. Results: The Tree HMM model predicts the break down point starts once patient is infected with the HIV virus as it affects the immune system. The immune system drops more quickly in the initial inter arrival time when compared with the later time interval. The HIV virus length in the nth state within regrouping is uncertain to occur in each state of the given model. A simulation study was done to assess the goodness of fit for the model. Conclusion: The HIV virus length in the nth state within regrouping is uncertain to occur in each state of the given model. The inter arrival censoring between each state is essential in each infected HIV patients. The outcome of this works states that health care expert can use this model for effective patient cares. Keywords: expectation, hidden markov model, human immunodeficiency virus, immune system, transition