A multilevel analysis of factors associated with wasting in children one to two years old in Hamadan city.

The study aimed to identify risk factors for childhood wasting in 1-2 year-olds in Hamadan city, focusing on this age group due to infection and malnutrition risks. Unlike previous cross-sectional studies on children under 5 years old, this longitudinal study tracked weight-to-height changes over time. Data were analyzed from 455 mother-child pairs, aged 1-2 years, collected from health centers and recorded in the Integrated Electronic Health System (SIB).

Risk factors associated with underweight in children aged one to two years: a longitudinal study.

Background: Underweight is a prevalent health issue in children. This study aimed to identify factors associated with underweight in children aged 1-2 years in Hamadan city. Unlike the studies conducted in this field, which are cross-sectional and do not provide information on the effect of age changes on underweight, our longitudinal approach provides insights into weight changes over time.

Predicting preeclampsia and related risk factors using data mining approaches: A cross-sectional study.

Background: Preeclampsia is a type of pregnancy hypertension disorder that has adverse effects on both the mother and the fetus. Despite recent advances in the etiology of preeclampsia, no adequate clinical screening tests have been identified to diagnose the disorder.

Objective: We aimed to provide a model based on data mining approaches that can be used as a screening tool to identify patients with this syndrome and also to identify the risk factors associated with it.

Random forest model to identify factors associated with anabolic-androgenic steroid use.

Background: One of the types of doping that is commonly used by bodybuilders, is androgenic-anabolic steroids (AAS). The use of AAS besides violating sporting ethics would have serious consequences on physical and mental health statuses. This study aimed to determine the most important factors of using AAS among bodybuilders by prototype willingness model (PWM).

The Prediction of Obstructive Sleep Apnea Using Data Mining Approaches.

Background: Obstructive sleep apnea (OSA) which is the most common sleep disorder breathing (SDB), imposes heavy costs on health and economy. The aim of this study was to provide models based on data mining approaches (C5.0 decision tree and logistic regression model [LRM]) and choose a top model for predicting OSA without polysomnography (PSG) devices that is a standard method for diagnosis of this disease, to identify patients with this syndrome payment.

Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea.

Background: Diagnosing of obstructive sleep apnea (OSA) is an important subject in medicine. This study aimed to compare the performance of two data mining techniques, support vector machine (SVM), and logistic regression (LR), in diagnosing OSA. The best-fit model was used as a substitute for polysomnography (PSG), which is the gold standard for diagnosing this disease.