ione pubblica e dall'immaginario collettivo. Il discorso dominante è così rappresentabile: il rischio sociale ed esistenziale avvolge le società globali e, quindi, il rischio sul lavoro va assunto in maniera aconflittuale come un dato naturale incoercibile. La sua invisibilità altro non sarebbe che l'unità di misura della sua nuova naturalità sociale. 57 Ibidem. 58 Ibidem. 59 Ibidem.

International Journal of Academic Health and Medical Research (IJAHMR) ISSN: 2000-007X Vol. 3 Issue 1, January – 2019, Pages: 9-12 www.ijeais.org/ijahmr 9 ANN for Predicting Birth Weight Mohammed Al-Shawwa Department of Information Technology, Faculty of Engineering and Information Technology, Al-Azhar University Gaza, Palestine Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in hospitals. The evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. Keywords: Artificial Neural Networks, Birth Weight, ANN, Predictive Model. 1. INTRODUCTION The main objective of a birth weight prediction system is to identify and the weight of the baby, Do you a normal weight or a low weight, and baby's low weight affects her life, such as injury squint. This study seeks to explore the possibility of using the artificial neural network model to predict the birth weight, at the lowest possible time and high accuracy in the results. Of course one would expect the birth weight to be associated with several influential factors as mentioned earlier. On the other hand it is clear that it will be very difficult to find a mathematical 