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Background and Objectives: The aim of this study was to explain the examples of "the Jurisprudential Rule of "Osr o- Haraj" or Denegation of Intolerable Hardship in the occurrence of congenital anomalies and genetic disorders and show that according to the current laws of the country, more of these diseases can be prevented. Material and Methods: Data for occurrence of congenital anomalies were derived from two major regional and national epidemiological studies based on 6,465,849 births in the country. A comprehensive review of literature was carried out on the details of 'Denegation of Intolerable Hardship Rule. The details of medical termination of pregnancies diagnosed for congenital anomalies and the role of 'Denegation of Intolerable Hardship' principle in the prevention of birth defects were explored in several Focused Group Discussions formed by peditricians, geneticists, psychiatrists, neotatalogists and expert jurisprudents. Results: The statistical estimates indicate that there are now nearly 100,000 births with one of the birth defects occurring every year in Iran. The total prevalence of the anomalies has more than tripled in the last 18 years. Conclusion: The occurrence of congenital anomalies and genetic disorders will undermine the gene pool of the population if preventive measures are not properly implemented in the community genetics services to control those disorders. According to the current law on therapeutic abortion, disorders that lead to the hardships of father, mother, family, community, as well as future generations may be diagnosed by the three specialists (before the 16th week of pregnancy, the time of onsetting the soul). They can then be referred for medical abortion. A minimum of 70,000 birth defects may be avoided using the therapeutic termination of pregnancies diagnosed for congenital anomalies if the 'no hardship and negation of distress and constriction' principle is suitably explored in maternal, pediatric, neonatal settings and for public health authorities. [ABSTRACT FROM AUTHOR]

Background and Objectives: The high rate of neonatal mortality is a major problem in health care systems all around the world. The accurate estimation of neonatal mortality is a prerequisite for the development of future health strategies that leads to the improvements in neonatal health. Providing a predictive model is, therefore, essential to reduce the neonatal mortality rate and reducing health care costs. The purpose of this study was to produce a model based on the data mining techniques to increase the accuracy of the prediction of the outcome of the neonatal mortality using a bagging neural network model in Rapidminer software. Material and Methods: This study was conducted on 8053 births (including 1605 cases and 6448 controls) across the country in 1394. The study variables including maternal diseases, mother age, gestational age, child gender, birth weight, birth order, abnormalities were selected as predictive factors for bagging neural network method. We compared bagging neural network with neural network, decision tree and nearest neighbor. Some criteria including the area under ROC curve, precision, accuracy and classification error rate were considered in comparing with other data mining models. Results: The comparison of bagging neural network with other data mining models showed that the bagging neural network gives better results compared to other models: precision (99.21), accuracy (99.17), classification error rate (0.83) and AUC value (0.992). Conclusion: We conclude that the bagging neural network may help to reduce the cost of health care system, and to improve the community health by preventing the mortality and adverse outcomes in neonates. [ABSTRACT FROM AUTHOR]