ABSTRACT
The aim of this study was to propose a method for improving the power of recognition and classification of thromboembolic syndrome based on the analysis of gene expression data using artificial neural networks
The studied method was performed on a dataset which contained data about 117 patients admitted to a hospital in Durham in 2009. Of all the studied patients, 66 patients were suffering from thromboembolic syndrome and 51 people were enrolled in the study as the control group
The gene expression level of 22277 was measured for all the samples and was entered into the model as the main variable. Due to the high number of variables, principal components analysis and auto-encoder neural network methods were used in order to reduce the dimension of data
The results showed that when using auto-encoder networks, the classification accuracy was 93.12. When using the PCA method to reduce the size of the data, the obtained accuracy was 78.26, and hence a significant difference in the accuracy of classification was observed. If auto-encoder network method is used, the sensitivity and specificity will be 92.58 and 93.68 and when PCA method is used, they will be 0.77 and 0.78 respectively. The results suggested that auto-encoder networks, compared with the PCA method, had a higher level of accuracy for the classification of thromboembolic syndrome status
ABSTRACT
The aim of this study was to comparison of depression and uncertainty in cardiac patient and normal persons. The present study has been done in a form of casual-comparative on 60 persons at the Bookan city hospital. In the present study, Beck Depression Inventory [1988], and Fristone Uncertainty Scale [1994] were employed to gather the required data. For data analysis, Manova, Pearson correlation coefficient and regression analysis methods by SPSS-19 was used. Finding supported, the rate of depression and uncertainty were higher in coronary heart disease than normal persons. There is meaningful relationship between depression and uncertainty [p=0.01]. The result of regression analysis showed that about 14% of depression variance was predicted by uncertainty. Results supported that depression and uncertainty are associated with CHD. These results for the use of psychological interventions focusing on depression and uncertainty in the prevention and treatment of Coronary Heart Disease can be important in treating cardiac patients.