Journal of Statistics Applications & Probability


Without a real reduction in population fertility rates, developing societies will push for more spending on their infrastructure and more demand for basic services for new-born, and more dependency and crowding, and the attendant ills and various social, economic and cultural problems, which will push these countries towards Directing a large part (if not most) of development revenues to meet the growing population. In general, the importance of this study lies in how to predict fertility rates using the rates of family planning methods (practice rates, years of protection) and to identify the method of neural networks and its accuracy in dealing with fertility data in particular. The study concluded that the prevalence of family planning methods (PR) and protection rate (CYP) are used to estimate and predict the total fertility rate (TFR) very efficiently, and artificial neu6ral networks have reached a high rate and high accuracy in estimating and predicting the total fertility rate (TFR) is highly and reliable (99.6%).

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