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. METHODS: We carried out a retrospective cross-sectional study on a dataset collected from Fatemieh Hospital in 2017 that included 741 mother-newborn pairs and 13 potential factors. Decision tree, random forest, artificial neural network, support vector machine, and logistic regression (LR) methods were used to predict LBW, with five evaluation criteria utilized to compare performance. RESULTS: Our findings revealed a 7% prevalence of LBW. The average accuracy of all models was 87% or higher. The LR method provided a sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and accuracy of 74%, 89%, 7.04%, 29%, and 88%, respectively. Using LR, gestational age, number of abortions, gravida, consanguinity, maternal age at delivery, and neonatal sex were determined to be the six most important variables associated with LBW. CONCLUSIONS: Our findings underscore the importance of facilitating timely diagnosis of causes of abortion, providing genetic counseling to consanguineous couples, and strengthening care before and during pregnancy (particularly for young mothers) to reduce LBW.

Investigating factors associated with the number of rehospitalizations among patients with schizophrenia disorder using penalized count regression models.

BACKGROUND: Schizophrenia is a chronic, severe, and debilitating mental disorder always considered one of the recurrent psychiatric diseases. This study aimed to use penalized count regression models to determine factors associated with the number of rehospitalizations of schizophrenia disorder. METHODS: This retrospective cohort study was performed on 413 schizophrenic patients who had been referred to the Sina (Farshchian) Educational and Medical Center in Hamadan, Iran, between March 2011 and March 2019. The penalized count regression models were fitted using R.3.5.2. RESULTS: About 73% of the patients were male. The mean (SD) of age and the number of rehospitalizations were 36.16 (11.18) years and 1.21 (2.18), respectively. According to the results, longer duration of illness (P < 0.001), having a positive family history of psychiatric illness (P = 0.017), having at least three children (P = 0.013), unemployment, disability, and retirement (P = 0.025), residence in other Hamadan province townships (P = 0.003) and having a history of arrest/prison (P = 0.022) were significantly associated with an increase in the number of rehospitalizations. CONCLUSION: To reduce the number of rehospitalizations among schizophrenic patients, it is recommended to provide special medical services for patients who do not have access to specialized medical centers and to create the necessary infrastructure for the employment of patients.

Application of group smoothly clipped absolute deviation method in identifying correlates of psychiatric distress among college students.

BACKGROUND: College students are at an increased risk of psychiatric distress. So, identifying its important correlates using more reliable statistical models, instead of inefficient traditional variable selection methods like stepwise regression, is of great importance. The objective of this study was to investigate correlates of psychiatric distress among college students in Iran; using group smoothly clipped absolute deviation method (SCAD). METHODS: A number of 1259 voluntary college students participated in this cross-sectional study (Jan-May 2016) at Hamadan University of Medical Sciences, Iran. The data were collected using a self-administered questionnaire consisting of demographic information, a behavioral risk factors checklist and the GHQ-28 questionnaire (with a cut-off of 23 to measure psychiatric distress, recommended by the Iranian version of the questionnaire). Penalized logistic regression with a group-SCAD regularization method was used to analyze the data (α = 0.05). RESULTS: The majority of students were aged 18-25 (87.61%), and 60.76% of them were female. About 41% of students had psychiatric distress. Significant correlates of psychiatric distress among college students selected by group-SCAD included the average grade, educational level, being optimistic about future, having a boy/girlfriend, having an emotional breakup, the average daily number of cigarettes, substance abusing during previous month and having suicidal thoughts ever (P < 0.05). CONCLUSIONS: Penalized logistic regression methods such as group-SCAD and group-Adaptive-LASSO should be considered as plausible alternatives to stepwise regression for identifying correlates of a binary response. Several behavioral variables were associated with psychological distress which highlights the necessity of designing multiple factors and behavioral changes in interventional programs.