Newborns prediction based on a belief Markov chain model

The prediction of numbers of newborns is an important issue in hospital management. Relying on the inherent non-aftereffect property, discrete-time Markov chain (DTMC) is a candidate for solving the problem. But the classical DTMC is unable to handle the uncertainty of states, especially when the state space is not discrete, which would lead to instable predicted results. In order to overcome the limitation of the existing DTMC model, a belief Markov chain (BMC) model is proposed by synthesizing the classical DTMC and Dempster-Shafer theory effectively. Depending on the advantages of Dempster-Shafer theory in expressing uncertainty, the proposed BMC model is capable of dealing with various uncertainties, which improves and perfects the classical DTMC model. An illustrative example demonstrates the effectiveness of the proposed model. Moreover, a comparison between the proposed BMC model and the classical and fuzzy states modified DTMC models is given to show the superiority of the proposed model against the other two. Finally, the stability of the proposed model has been proven.