No abstract

Introduction:Gestational diabetes mellitus (GDM) is state of carbohydrate intolerance detected first time during pregnancy. GDM represents a significant risk factor for the development of CVD in women. The degree to which women with histories of gestational diabetes are at risk for cardiovascular disease, beyond their predisposition to future diabetes, is still unclear. The aim of our study was to assess the presence of surrogate markers of subclinical atherosclerosis which can be present in them even without developing type 2 diabetes.Subjects and Methods:In this descriptive cross-sectional hospital based study, 50 patients 20-45 yrs of age, premenopausal, at least 1 yr past her most recent pregnancy, and not more than 5 yr past her index pregnancy with GDM. These patients and controls who did not have GDM were assessed for carotid intima media thickness,endothelial dysfunction, epicardial fat thickness and other cardiovascular risk factors.Results:Women with pGDM were found to have unfavourable cardiovascular risk parameters. They also demonstrated more frequent occurrence of metabolic syndrome(64% vs 10%) than control subjects. Individual components of MS increased with increasing BMI in both the groups. As far as markers of subclinical atherosclerosis were concerned women with pGDM had significantly higher CIMT, FMD and epicardial fat thickness than control group.Conclusion:Women with pGDM, even before development of diabetes have significant differences in CVD risk factors when compared to those who do not have such history. Postpartum screening for glucose intolerance and efforts to minimize modifiable cardiovascular risk factors, including hypertension, viscerall adiposity, and dyslipidemia should be the most effective measures for lowering of cardiovascular risk.

Background: The modern society is extremely prone to many life-threatening diseases, which can be easily controlled as well as cured if diagnosed at an early stage. The development and implementation of a disease diagnostic system have gained huge popularity over the years. In the current scenario, there are certain factors such as environment, sedentary lifestyle, genetic (hereditary) are the major factors behind the life threatening disease such as, ‘diabetes’. Moreover, diabetes has achieved the status of the modern man’s leading chronic disease. So one of the prime need of this generation is to develop a state-of-the-art expert system which can predict diabetes at a very early stage with a minimum of complexity and in an expedited manner. The primary objective of this research work is to develop an indigenous and efficient diagnostic technique for detection of the diabetes. Method & Discussion: The proposed methodology comprises of two phases: In the first phase The Pima Indian Diabetes Dataset (PIDD) has been collected from the UCI machine learning repository databases and Localized Diabetes Dataset (LDD) has been gathered from Bombay Medical Hall, Upper Bazar Ranchi, Jharkhand, India. In the second phase, the dataset has been processed through two different approaches. The first approach entails classification through Adaboost, Classification Via Regression (CVR), Radial Basis Function Network (RBFN), K-Nearest Neighbor (KNN) on Pima Indian Diabetes Dataset and Localized Diabetes Dataset. In the second approach, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have been applied as a feature reduction method followed by using the same set of classification methods used in the first approach. Among all of the implemented classification method, PCA_CVR performs the highest performance for both the above mentioned dataset. Conclusion: In this research article, comparative analysis of outcomes obtained by with and without the use of PCA and LDA for the same set of classification method has been done w.r.t performance assessment. Finally, it has been concluded that PCA & LDA both is useful to remove the insignificant features, decreasing the expense and computation time while improving the ROC and accuracy. The used methodology may similarly be applied in other medical diseases.

No abstract

Background: Classification method is needed to deduce the possible errors and assist the doctor’s. These methods are used in every many of our lives to take suitable decisions. It is well known that classification is an efficient, effective and broadly utilized strategy in several applications such as medical disease diagnosis, etc. The prime objective of this research paper is to achieve an efficient and effective classification method for Diabetes. Discussion: The proposed methodology comprises of two phases: The first phase deals with description of Pima Indian Diabetes Dataset and Localized Diabetes Dataset whereas in the second phase dataset has been processed through two different approaches. First approach entails classification through Polynomial Kernel, RBF Kernel, Sigmoid Function Kernel and Linear Kernel SVM on Pima Indian Diabetes Dataset and Localized Diabetes Dataset. In the second approach, PSO have been utilized as a feature reduction method followed by using the same set of classification methods used in the first approach. PSO_Linear Kernel SVM provides the highest accuracy and ROC for both the above mentioned dataset. Conclusion: In this research paper, comparative analysis of outcomes w.r.t. performance assessment has been done using both with PSO and without PSO for the same set of classification methods. Finally, it has been concluded that PSO is selecting the relevant features, reducing the expense and computation time while improving the ROC and accuracy. The used methodology may similarly be implemented in other medical diseases.