Machine Learning-based Classifiers for the Prediction of Low Birth Weight.

Objectives: Low birth weight (LBW) is a global concern associated with fetal and neonatal mortality as well as adverse consequences such as intellectual disability, impaired cognitive development, and chronic diseases in adulthood. Numerous factors contribute to LBW and vary based on the region. The main objectives of this study were to compare four machine learning classifiers in the prediction of LBW and to determine the most important factors related to this phenomenon in Hamadan, Iran.

Identifying factors associated with the hospital readmission rate among patients with major depressive disorder.

Background: Major depressive disorder (MDD) is a common recurrent mental disorder and one of the leading causes of disability in the world. The recurrence of MDD is associated with increased psychological and social burden, limitations for the patient, family, and society; therefore, action to reduce and prevent the recurrence of this disorder or hospital readmissions for depression among the patients is essential.

Methods: The data of this retrospective cohort study were extracted from records of 1005 patients with MDD hospitalized in Farshchian hospital in Hamadan city, Iran (2011-2018).

Determining correlates of the average number of cigarette smoking among college students using count regression models.

College students, as a large part of young adults, are a vulnerable group to several risky behaviors including smoking and drug abuse. This study aimed to utilize and to compare count regression models to identify correlates of cigarette smoking among college students. This was a cross-sectional study conducted on students of Hamadan University of Medical Sciences.