Objectives: Reducing infant mortality in the whole world is one of the millennium development goals.The aim of this study was to determine the factors related to infant mortality using data mining algorithms. Methods: This population-based case-control study was conducted in eight provinces of Iran. A sum of 2,386 mothers (1,076 cases and 1,310 controls) enrolled in this study. Data were extracted from health records of mothers and filled with checklists in health centers. We employed several data mining algorithms such as AdaBoost classifier, Support Vector Machine, Artificial Neural Networks, Random Forests, K-nearest neighborhood, and Naïve Bayes in order to recognize the important predictors of infant death; binary logistic regression model was used to clarify the role of each selected predictor. Results: In this study, 58.7% of infant mortalities occurred in rural areas, that 55.6% of them were boys. Moreover, Naïve Bayes and Random Forest were highly capable of predicting related factors among data mining models. Also, the results showed that events during pregnancy such as dental disorders, high blood pressure, loss of parents, factors related to infants such as low birth weight, and factors related to mothers like consanguineous marriage and gap of pregnancy (< 3 years) were all risk factors while the age of pregnancy (18 - 35 year) and a high degree of education were protective factors. Conclusions: Infant mortality is the consequence of a variety of factors, including factors related to infants themselves and their mothers and events during pregnancy. Owing to the high accuracy and ability of modern modeling compared to traditional modeling, it is recommended to use machine learning tools for indicating risk factors of infant mortality.

Background: The Coronavirus Disease 2019 (COVID-19) pandemic and imposed quarantine have had different effects on the social and psychological aspects of people. The lack of any definitive treatment or preventive method for COVID-19 has caused a great deal of stress and anxiety in people. Objective: The aim of this study was to investigate the demographic characteristics and common complaints of callers to the telephone counseling helpline to receive services for anxiety and stress caused by COVID-19. Methods: This is a descriptive cross-sectional study. The study samples were 1978 callers to the telephone counseling helpline of the Academic Center for Education, Culture and Research (ACECR) in Iran. Data collection was done by a checklist made by researchers and provided to the consultants. Findings: Most of callers (65.8%) were women and married (77.7%) with a mean age of 44.14 years; 41.5% of callers with no any symptoms were afraid and worried about getting infected; 26.6% stated their anxiety was due to worry that their first-degree relatives may get infected, and 8.5% reported that their anxiety was because of fear of economic problems and loss of job or income . Conclusion: The main users of the telephone counseling helpline was married women aged 30-39 years. Considering the anxiety and stress caused by COVID-19 outbreak, it seems necessary to provide counseling services.