Fariba Nabatchian, Nahid Einollahi, Mohammad Ali Boroomand, Sakineh Abbasi,
Volume 7, Issue 2 (7-2013)
Abstract
Background and Aim: Oxidative interactions such as the formation of oxygen, peroxy radicals and LDL-cholesterol oxidation are involved in the development of atherosclerosis process
This study aims to examine the relationship between serum bilirubin levels and the incidence of coronary artery disease.
Materials and Methods: Eighty-five patients and ninety-two healthy volunteers were enrolled in this study. Total and direct bilirubin levels were measured using diazo method. Besides, triglycerides and total cholesterol were determined by enzymatic method, HDL-Cholesterol by polyanionic method, and LDL-Cholesterol by direct method. For statistical analysis of data, SPSS 17 was applied. For qualitative variables, Chi-square and for quantitative variables, t-student tests were used. The significance level was set at P=0.05.
Results: Direct, indirect and total bilirubin levels were 0.213, 0.375, 0.588 mg/dl for control group and 0.228, 0.365, 0.593 mg/dl for patient group, respectively. No significant difference was observed between the mean values for direct, indirect and total bilirubin in the two groups. Furthermore, there was no significant difference between triglycerides and total cholesterol level figures in the two groups. However, there was a significant difference between HDL-Cholesterol levels (P=0.001), smoking (P=0.031), family history (P=0.006), and mean blood pressure (P<0.001) of the two groups.
Conclusion: The results of this study indicate that measurement of bilirubin as a marker for predicting coronary artery disease may be important. In the end, it should be mentioned that the findings of this study are consistent with some previous studies, but incompatible with others in this area.
Reza Safdari, Maliheh Kadivar, Parinaz Tabari, Hala Shawky Own ,
Volume 11, Issue 5 (1-2018)
Abstract
Background and Aim: Neonatal jaundice is a matter that is very important for clinicians all over the world because this disease is one of the most common cases that requires clinical care. The aim of this study is to use data classification algorithms to predict the type of jaundice in neonates, and therefore, to prevent irreparable damages in future.
Materials and Methods: This is a descriptive study and is done with the use of neonatal jaundice dataset that has been collected in Cairo, Egypt. In this study, after preprocessing the data, classification algorithms such as decision tree, Naïve Bayes, and kNN (k-Nearest Neighbors) were used, compared and analyzed in Orange application.
Results: Based on the findings, decision tree with precision of 94%, Naïve Bayes with precision of 91%, and kNN with precision of 89% can classify the types of neonatal jaundice. So, among these types, the most precise classification algorithm is decision tree.
Conclusion: Classification algorithms can be used in clinical decision support systems to help physicians make decisions about the types of special diseases; therefore, physicians can look after patients appropriately. So the probable risks for patients can be decreased.
