Pattern of weight gain in pregnant women in slum areas of Hamadan using multilevel ordinal regression.

BACKGROUND: Adequate gestational weight gain (GWG) is an important factor for maternal and fetal health. This is especially important in low-income and slum areas due to limited access to health services and malnutrition. Thus, the purpose of this study is to evaluate the pattern of GWG changes in the slum areas of Hamadan in Iran. METHODS: In this longitudinal study, the study sample consisted of 509 pregnant women who referred to nine health care clinics in the slum areas of Hamadan. Women's weight gain based on the recommended GWG by U.S. Institute of Medicine (IOM) was divided into three categories: Inadequate weight gain, Adequate weight gain, and Excessive weight gain. In order to evaluate the trend of GWG, a multi-level ordinal model was used. RESULTS: According to pre-pregnancy BMI, a little more than half people (56.6%) were overweight or obese. 85.4% women in the first trimester and 49.1% in the second trimester did not have adequate GWG, but in the third trimester (38.9%) had adequate GWG. Based on multivariate analysis, pre- pregnancy BMI has a significant effect on the odds of inadequate GWG (P-value = 0.021); with one unit increase in pre-pregnancy BMI, the odds of inadequate GWG grows by 1.07 times compared to adequate and excessive GWG. CONCLUSIONS: In general, women did not have adequate weight gain in the first and second trimesters.Thus, designing appropriate interventions to achieve optimal GWG seems to be necessary in slums.

Modeling of smoking intensity by age at smoking onset among Iranian adult male using generalized additive model.

Because the age at which a person first starts smoking has such a strong correlation with future smoking behaviours, it's crucial to examine its relationship with smoking intensity. However, it is still challenging to accurately prove this relationship due to limitations in the methodology of the performed studies. Therefore the main purpose of this study is to evaluate the potential risk factors affecting the intensity of smoking, especially the age of smoking onset among Iranian adult male smokers over 18 years of age using a generalized additive model (GAM). In GAM a latent variable with logistic distribution and identity link function was considered. Data from 913 Iranian male current smokers over the age of 18 was evaluated from a national cross-sectional survey of non-communicable disease (NCD) risk factors in 2016. Individuals were classified into: light, moderate, and heavy smokers. A GAM was used to assess the relationship. The results showed that 246 (26.9%) subjects were light smokers, 190 (20.8%) subjects were moderate smokers and 477 (52.2%) subjects were heavy smokers. According to the GAM results, the relationship was nonlinear and smokers who started smoking at a younger age were more likely to become heavy smokers. The factors of unemployment (OR = 1.364, 95% CI 0.725-2.563), retirement (OR = 1.217, 95% CI 0.667-2.223), and exposure to secondhand smoke at home (OR = 1.364, 95% CI 1.055-1.763) increased the risk of heavy smoking. but, smokers with high-income (OR = 0.742, 95% CI 0.552-0.998) had a low tendency to heavy smoking. GAM identified the nonlinear relationship between the age of onset of smoking and smoking intensity. Tobacco control programs should be focused on young and adolescent groups and poorer socio-economic communities.

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). METHODS: In this analytical cross-sectional study, 280 male bodybuilders were selected from the bodybuilding clubs in Hamadan city using multistage sampling in 2016. A self-administered questionnaire consisting of demographic information and constructs of the PWM was then used to collect data and random forest model was also applied to analyze the collected data. RESULTS: Behavioral willingness, attitude, and previous AAS use were found as the most important factors in determining the behavioral intention. Moreover, subjective norms, attitude, BMI, and prototypes were the factors with the greatest effect on predicting behavioral willingness of AAS use. As well, behavioral intention was observed to be more important than behavioral willingness for predicting of AAS use. DISCUSSION: The obtained results show that the reasoned action path has a greater impact to predict AAS use among bodybuilders compared to social reaction path.

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. MATERIALS AND METHODS: The data used to perform this cross-sectional study were extracted from the clinical records of 726 mothers with preeclampsia and 726 mothers without preeclampsia who were referred to Fatemieh Hospital in Hamadan City during April 2005-March 2015. In this study, six data mining methods were adopted, including logistic regression, k-nearest neighborhood, C5.0 decision tree, discriminant analysis, random forest, and support vector machine, and their performance was compared using the criteria of accuracy, sensitivity, and specificity. RESULTS: Underlying condition, age, pregnancy season and the number of pregnancies were the most important risk factors for diagnosing preeclampsia. The accuracy of the models were as follows: logistic regression (0.713), k-nearest neighborhood (0.742), C5.0 decision tree (0.788), discriminant analysis (0.687), random forest (0.758) and support vector machine (0.791). CONCLUSION: Among the data mining methods employed in this study, support vector machine was the most accurate in predicting preeclampsia. Therefore, this model can be considered as a screening tool to diagnose this disorder.

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. METHODS: In this cross sectional study, data was extracted from the medical records of 333 patients with sleep disorders who were referred to sleep disorders research center of Kermanshah University of Medical Sciences during the years 2012-2016. All patients underwent one night PSG. A stepwise LRM was fitted and its performance was compared with C5.0 decision tree with use of the criteria of accuracy, sensitivity and specificity. RESULTS: For C5.0 decision tree, accuracy was obtained 0.757 with 95% confidence interval (0.661, 0.838), sensitivity was 0.66 and specificity was 0.809. For LRM, these items were obtained 0.737 (0.639, 0.820), 0.693 and 0.78 respectively. CONCLUSION: C5.0 decision tree showed better performance than LRM in diagnosis of OSA. So this model can be considered as an alternative approach for PSG.

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. MATERIALS AND METHODS: A total of 250 patients with sleep problems complaints and whose disease had been diagnosed by PSG and referred to the Sleep Disorders Research Center of Farabi Hospital, Kermanshah, between 2012 and 2015 were recruited in this study. To fit the best LR model, a model was first fitted with all variables and then compared with a model made from the significant variables using Akaike's information criterion (AIC). The SVM model and radial basis function (RBF) kernel, whose parameters had been optimized by genetic algorithm, were used to diagnose OSA. RESULTS: Based on AIC, the best LR model obtained from this study was a model fitted with all variables. The performance of final LR model was compared with SVM model, revealing the accuracy 0.797 versus 0.729, sensitivity 0.714 versus 0.777, and specificity 0.847 vs. 0.702, respectively. CONCLUSION: Both models were found to have an appropriate performance. However, considering accuracy as an important criterion for comparing the performance of models in this domain, it can be argued that SVM could have a better efficiency than LR in diagnosing OSA in patients.