Prediction of compressibility parameters of the soils using artificial neural network.

The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.

Physiological parameters as a tool in the diagnosis of fibromyalgia syndrome in females: A preliminary study.

AIMS: Although fibromyalgia (FM) syndrome is associated with many symptoms, there is as yet no specific finding or laboratory test diagnostic of this syndrome. The physical examination and laboratory tests may be helpful in figuring out this syndrome. MATERIALS AND METHODS: The heart rate, respiration rate, body temperature (TEMP), height, body weight, hemoglobin level, erythrocyte sedimentation rate, white blood cell count, platelet count (PLT), rheumatoid factor and C-reactive protein levels and electrocardiograms (ECG) of FM patients were compared with those of control individuals. In addition, the predictive value of these tests was evaluated via receiver operating characteristic (ROC) analysis. KEY FINDINGS: The results showed that the TEMP and the PLT were higher in the FM group compared with the control group. Also, ST heights in ECGs which corresponds to a period of ventricle systolic depolarization, showed evidence of a difference between the FM and the control groups. There was no difference observed in terms of the other parameters. According to the ROC analysis, PLT, TEMP and ST height have predictive capacities in FM. SIGNIFICANCE: Changes in hormonal factors, peripheral blood circulation, autonomous system activity disorders, inflammatory incidents, etc., may explain the increased TEMP in the FM patients. The high PLT level may signify a thromboproliferation or a possible compensation caused by a PLT functional disorder. ST depression in FM patients may interrelate with coronary pathology. Elucidating the pathophysiology underlying the increases in TEMP and PLT and the decreases in ST height may help to explain the etiology of FM.

A Study on the Effects of Sympathetic Skin Response Parameters in Diagnosis of Fibromyalgia Using Artificial Neural Networks.

Fibromyalgia syndrome (FMS), usually observed commonly in females over age 30, is a rheumatic disease accompanied by extensive chronic pain. In the diagnosis of the disease non-objective psychological tests and physiological tests and laboratory test results are evaluated and clinical experiences stand out. However, these tests are insufficient in differentiating FMS with similar diseases that demonstrate symptoms of extensive pain. Thus, objective tests that would help the diagnosis are needed. This study analyzes the effect of sympathetic skin response (SSR) parameters on the auxiliary tests used in FMS diagnosis, the laboratory tests and physiological tests. The study was conducted in Suleyman Demirel University, Faculty of Medicine, Physical Medicine and Rehabilitation Clinic in Turkey with 60 patients diagnosed with FMS for the first time and a control group of 30 healthy individuals. In the study all participants underwent laboratory tests (blood tests), certain physiological tests (pulsation, skin temperature, respiration) and SSR measurements. The test data and SSR parameters obtained were classified using artificial neural network (ANN). Finally, in the ANN framework, where only laboratory and physiological test results were used as input, a simulation result of 96.51 % was obtained, which demonstrated diagnostic accuracy. This data, with the addition of SSR parameter values obtained increased to 97.67 %. This result including SSR parameters - meaning a higher diagnostic accuracy - demonstrated that SSR could be a new auxillary diagnostic method that could be used in the diagnosis of FMS.

The correlation of laboratory tests and Sympathetic Skin Response parameters by using artificial neural networks in fibromyalgia patients.

Fibromyalgia syndrome (FMS) is a chronic musculoskeletal disease which causes dysfunction of the autonomic nervous system. Sympathetic Skin Response (SSR) is a part of electrical impedance of body which is affected by the autonomic nervous system dysfunctions. In this study, values obtained from the results of the patients diagnosed with fibromyalgia syndrome, and healthy subjects blood samples in the laboratory conditions are recorded in Suleyman Demirel University, Faculty of Medicine, Department of Physical Medicine and Rehabilitation. SSR measurements are recorded from patients and healthy controls. Values of latency time, maximum amplitude and elapsed time between two stimulus parameters are obtained from recorded sympathetic skin response data by using Matlab software. The relationship between SSR parameters and laboratory tests is investigated by using artificial neural networks. As a result SSR seems to be a valid parameter in the classification of FMS.

Measuring the sympathetic skin response on body and using as diagnosis-purposed for lung cancer patients by artificial neural networks.

In this study, the points of Sympathetic skin response that can be measured from different zones on body of healthy and patient subjects are determined. The Sympathetic skin responses on these points are obtained using a measurement device that is called Grass Model 7 Polygraph 1. The database is formed in Cerrahpasa University, Faculty of Medicine and data is taken from healthy and patient subjects who are volunteer. Some parameters of the subjects which are more effective on SSR such as height, weight, age must be chosen between the specific limits to obtain results more clearly. The symmetric points on human body are chosen for the measurement. After that, the Sympathetic skin response values which are measured from a human body are simulated and tested by using artificial neural network toolbox on Matlab software. The structure of the chosen neural network is a multilayer feedforward neural network. According to simulation results, the application method as diagnosis-purposed of the lung cancer patients is explained by using the differences related to these values on the skin.