Risk factors of Non-fatal Unintentional Home Injuries among Children under 5 Years Old; a Population-Based Study

Introduction: In addition to the annual mortality rate, unintentional home injury may result in temporary orpermanent disability and requires medical attention and continuous care in millions of children. This studyaimed to explore features and risk factors of these injuries. Methods: In this cross-sectional study, demographicvariables and epidemiologic pattern of home injuries among children under 5 years of age were collected via apopulation-based survey in sevenmain cities of Khuzestan province, southwest Iran, during September 2011 toDecember 2012. Developing a risk stratification model, independent risk factors of unintentional home injurywere determined and put to multivariate logistic regression analysis. Results: 2693 children with the mean ageof 27.36 § 15.55 months (1 to 60) were evaluated (50.9% boy). 827 (30.7%) cases had a history of at least onehome injury occurrence since birth to study time. The most common injury mechanisms were burning with 291(38.4%) cases, falling with 214 (28.3%) and poisoning with 66 (8.7%) cases, respectively. The independent riskfactors of unintentional home injury were age ¸ 24 month (p<0.001), residency in Ahvaz city (p<0.001), mother’silliteracy (p<0.014), ethnicity (p<0.001), private housing (p=0.01), birthweight (p<0.001), and being the first child(p=0. 01). Sensitivity, specificity, and area under the ROC curve of the model designed by multivariate analysiswere 53.5%, 84.8%, and 0.75 (95% CI: 0.73- 0.77; P < 0.001, figure 1), respectively. Conclusion: According tothe findings of this study, 30.7% of the studied children were injured at least once since birth. Burning, falling,poisoning, swallowing objects, choking, and biting were the main home injury mechanisms. Age ¸ 24 months,being the first child, living in a private house, being a resident of Ahvaz city, and having an illiterate mother werefound to be risk factors of home injury

Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases

Heart diseases are among the nation’s leading couse of mortality and moribidity. Data mining teqniques can predict the likelihood of patients getting a heart disease. The purpose of this study is comparison of different data mining algorithm on prediction of heart diseases. This work applied and compared data mining techniques to predict the risk of heart diseases. After feature analysis, models by five algorithms including decision tree (C5.0), neural network, support vector machine (SVM), logistic regression and k-nearest neighborhood (KNN) were developed and validated. C5.0 Decision tree has been able to build a model with greatest accuracy 93.02%, KNN, SVM, Neural network have been 88.37%, 86.05% and 80.23% respectively. Produced results of decision tree can be simply interpretable and applicable; their rules can be understood easily by different clinical practitioner

Lipid Profile and the Risk of Stroke: A Study from North of Iran

Stroke is the second cause of mortality in the world and third in Iran and lipid abnormalities are the main cause of stroke. The relation of dyslipidemia and the risk of stroke is mater of controversy. The aim of this paper is to determine the relationship of dyslipidemia and the risk of stroke in Sayad Shirazi hospital, Gorgan, Northeastern Iran. Retrospectively, we investigated all medical records with a diagnosis of stroke based on International Classification of Diseases, Revision 10, from August 2015 to August 2016 in Sayyad Shirazi hospital. We include those records with laboratory reports on serum lipid profile. The National Cholesterol Education Program Adult Treatment Panel III guideline was used to classifying lipid profile. The Data management and analysis was performed using SPSS 20. Out of 415 identified records, 9.6% had an unspecified diagnosis of stroke subtype. Only, in 160 records the lipid parameters were measured. The majority of cases with dyslipidemia was men (56.6%) and age older than 60 years (71%). There was a significant difference between ethnic groups and dyslipidemia (p=0.04) and between discharge outcome and lipid profile in women (p=0.05). Furthermore, the relation between dyslipidemia and another comorbid risk factor for stroke including diabetes (p=0.004), ischemic heart disease (0.035), and prior stroke (0.002) was significant. This study has shown that dyslipidemia coexisting with diabetes, ischemic heart diseases, and prior stroke increases the risk of stroke especially in older age. In general, therefore, it seems that lipid-lowering therapy must be one of the priorities in this population

An integrated supervised and unsupervised learning approach to predict the outcome of tuberculosis treatment course

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Type of Pregnancy in Women Equal or Greater Than 35 Years of Age

Abstract — Few studies focused on unwanted pregnancy and predictive factors in the late age of reproduction. This study applied feed-forward neural network algorithm with ten-sigmoid function in a hidden and an output layer with 150 neurons to develop a predictive model for type of pregnancy. Data of 1404 women in Khuzestan province of Iran in age 35 or more were collected and eight attributes were selected. The model was developed in MATLAB. The results of this classification task showed about 82 % accuracy, 76% specificity and 56 % sensitivity. The model had an area under the curve of 0.67 (95 % CI: 0.64–0.70) to predict unwanted pregnancy for the optimum cut point. The model creates an opportunity to discriminate type of pregnancy with 80 % accuracy whether or not an individual is going to experience an unwanted pregnancy. This might be a criterion to find risky cases for unwanted pregnancy and then to select appropriate interventions for risky cases to prevent unwanted pregnancy occurrence. Keywords-artificial neural network; Iran; Prediction; unwanted pregnancy I