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Millions of people are diagnosed every year with a chest disease in the world. Chronic obstructive pulmonary and pneumonia diseases are two of the most important chest diseases. And these are very common illnesses in Turkey. In this paper, a comparative study on chronic obstructive pulmonary and pneumonia diseases diagnosis was realized by using neural networks and artificial immune systems. For this purpose, three different neural networks structures and one artificial immune system were used. Used neural network structures in this study were multilayer, probabilistic, and learning vector quantization neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on chronic obstructive pulmonary and pneumonia diseases diagnosis. The chronic obstructive pulmonary and pneumonia diseases dataset were prepared from a chest diseases hospital’s database using patient’s epicrisis reports. [ABSTRACT FROM AUTHOR]

Due to the importance of cirrhosis evolution, this study examined cirrhotic patients using Self Organizing Mapping (SOM) based on the Child-Pugh scoring method. Because Colored Doppler Ultrasound (CDU) has too many parameters, scoring can be a very difficult task. Classifying cirrhotic patients via SOM and investigating weights of the cirrhotic CDU parameters are aimed in this study. SOM was used to map high dimensional cirrhotic data onto two dimensional clustered data. These clusters provided a feature map of cirrhotic patients. In this study, 103 cirrhotic patients and a control group of 44 healthy individuals were examined in the hospital, and parameters were analyzed using SOM. These data were obtained using CDU, and age and sex parameters were analyzed in this study. Cirrhotic patients were histopathologically separated into subgroups using the Child-Pugh scoring method, and the presence of ascites was determined using SOM. In this study, differences between the control group and cirrhotic patients with their subgroups were investigated using SOM, and the results were discussed. Renal artery indices, hepatic artery indices, portal vein parameters, age and the degree of ascites were analyzed using SOM for a total of 147 individuals. The combination of SOM and Child-Pugh scoring method can be useful for the interpretation of cirrhotic patient's evolution. Computer-based SOM algorithm and negative effectiveness of a large scale dataset could be minimized by adjusting the weight of the parameters. This study will faciliate doctors to make better decisions for their patients. [ABSTRACT FROM AUTHOR]

The article presents a research study designed to use Singular Spectrum Analysis (SSA) to segments of sleep electroencephalogram (EEG) collected from healthy volunteers and patients diagnosed with psycho physiological insomnia or paradoxical insomnia. Topics covered include the different aspects of sleep EEG and SSA, the materials and methods used in the study, and details relating to the Artificial Neural Network (ANN) architectures used for EEG classification.

The article presents the study results on the use of multilayer neural networks (MLNNs) with genetic algorithm on the diagnosis of tuberculosis disease. Topics discussed include the reference to the mycobacterium tuberculosis with its limited treatment capacities in developing countries, the two hidden layers with the training algorithm and the success of classification in comparison with reference studies. Also mentioned was the Levenberg-Marquardt training algorithm.

The aorta is the largest vessel in the systemic circuit. Its diameter is very important to guess for child before adult age, due to growing up body. Aortic diameter, one of the cardiac values, changes in time. Evaluation of the cardiac structures and generating a valid regional curve requires a large study group experience for accurate data on normal values. In this study, our aim is to estimate aortic diameter values without curve of charts. Using real sample of the all groups has been predicted using a hybrid system based on combination of Line Based Normalization Method (LBNM) and Artificial Neural Network (ANN) with Levenberg–Marquardt (LM) algorithm. In this study, aortic diameter values dataset divided into two groups as 50% training–50% testing of whole dataset. In order to show the performance of the proposed method, two fold cross validation and prevalent performance measuring methods, Mean Square Error (MSE), Absolute Deviation (AD), Root Mean Square Error (RMSE), statistical relation factor T and R2, have been used. The obtained MSE results from combination of Min–Max normalization and ANN, combination of Decimal Scaling and ANN, combination of Z-score and ANN, and combination of LBNM and ANN (the proposed method) are 0.00517, 0.001299, 0.006196, and 0.000145, respectively. For the suggested method, error’s results have been given discretely for every age up to adult age. The results are compared to real aortic diameter values by expert with nine year experiences in medical area. These results have shown that the proposed method can be confidently used in the prediction of aortic diameter values in healthy Turkish infants, children and adolescents. [ABSTRACT FROM AUTHOR]