DEVELOPMENT OF DEEP LEARNING MULTI – LAYER PERCEPTRON MODEL USING NEGATIVE BINOMIAL REGRESSION APPROACH FOR WAITING TIME PREDICTION

Evidently queuing model has been very useful in identifying appropriate levels of staff, equipment, and beds along with in making decisions about resource allocation and the design of new services as well as Waiting Time (WT) estimation and predictions. However, the traditional Queuing Theory approach has been known not to be sufficient in real life applications because the methodology is limited, for instance, unrealistic assumptions of the time distribution it requires to do queuing analysis. Thus, the goal of this study is to develop deep learning multi – layer perceptron model using negative binomial regression approach for waiting time prediction and examine the existing used ML algorithms for WT prediction at different sample sizes of queuing and make an essential tool for responsive actions for any organization (i.e. healthcare centers) reporting long waiting times. The study sets to follow Monte Carlos Simulation process and utilization of real-life data. Sequel to the developed multi-layer poisson regression (MLP-PR) and following the shortcomings of poisson regression to account for overdispersion in count data as well as to regularize the problem of over-fitting in MLP approach, this pursues to introduce a Novel Multi-Layer Perceptron Negative Binomial Regression (MLP-NBR)for WT Prediction. The Novel model is expected to handle problem of overdispersion, autocorrelation and over-fitting more effectively compare to the MLP-PR.