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Ensemble learning methods are one of the most powerful tools for the pattern classification problems. In this paper, the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection were investigated. Six feature set models were constructed including different physical parameters and they fed into the ensemble classifiers as input features. As ensemble learning techniques, bagging, gradient boosting and random subspace (RSM) were used. Instance based learning (IBk) and random forest (RF) classifiers applied to six feature set models. The patients were classified into three groups such as osteoporosis, osteopenia and control (healthy), using ensemble classifiers. Total classification accuracy and f-measure were also used to evaluate diagnostic performance of the proposed ensemble classification system. The classification accuracy has reached to 98.85 % by the combination of model 6 ( five BMD + five T-score values) using RSM-RF classifier. The findings of this paper suggest that the patients will be able to be warned before a bone fracture occurred, by just examining some physical parameters that can easily be measured without invasive operations. [ABSTRACT FROM AUTHOR]

In these days, there are many various diseases, whose diagnosis is very hardly. Breast cancer is one of these type diseases. In this paper, accuracy diagnosis of normal, benign, and malign breast cancer cell were found by combining mean success rates Jensen Shannon, Hellinger, and Triangle measure which connected with each other. In this article, an diagnostic method based on feature extraction Discrete Wavelet Entropy Energy (DWEE) and Jensen Shannon, Hellinger, Triangle Measure (JHT) Classifier for diagnosis of breast cancer. This diagnosis method is called as DWEE-JHT this paper. With this diagnosis method have found optimal feature subset using discrete wavelet transform feature extraction. Then these convenient features are given to Jensen Shannon, Hellinger, Triangle Measure (JHT) classifier. Then, between classifiers which are Jensen Shannon, Hellinger, and triangle distance have been validated the measures via relationships. Afterwards, breast cancer cells are classified using Jensen Shannon, Hellinger, and Triangle distance. Mean success rate of 16 feature vector with Jensen Shannon classifier is found % 97.81. Mean success rate of 16 feature vector with Hellinger classifier is found % 97.75. Mean success rate of 16 feature vector with Triangle classifier is found % 97.87. By averaging of results obtained from these 3 classifiers are found as 97.81 % average of accuracy diagnosis. [ABSTRACT FROM AUTHOR]

With this paper it was tried to give a broad perspective about the responsiveness level of Turkish health care system, and how Turkish health care system meets the expectations of its citizens. For achieving the main purpose of this study a sample of hospital managers ( n = 172) was selected, and the responsiveness questionnaire developed by WHO was administered. If the responsiveness level of Turkish health care system is measured on the basis of the Turkish hospital managers’ views in the sample by using the weights reported in the World Health Report 2000, responsiveness level is calculated as 6.14, and Turkey's place should be 35 rather than 93 among 191 countries. The findings showed that Turkish health care system met confidentiality expectations of Turkish citizens better than other expectations for other aspects of responsiveness. In light of the main results of this study we conclude that the economic status, demographic structure, culture, and some other regional and country-specific factors should be taken into account by calculating and especially ranking the countries according to responsiveness level of their health care systems, and each country should carry out this kind of studies by using the views of their own key informants or preferably citizens, if possible. [ABSTRACT FROM AUTHOR]

Little is known about the factors that can affect informal health payments in Turkey, which is pertinent given that Turkey has been undergoing and considering a series of reforms in the health sector. This paper aims to examine the predictors of informal payments in Turkey. The study surveyed a random sample of 3,727 people in a medium-sized city, of whom 706 received medical care for illness and/or non-illness related reasons, using a Turkey-specific version of a model questionnaire focused exclusively on informal out-of-pocket payments. Using multivariable logistic model, household health expenditure, health insurance, service type and provider ownership were found to be statistically significant predictors of informal payment. The findings suggest the supply side factors as the main drivers of informal payments and thus a need for radical changes in the supply side as well as actions to rebuild lost confidence in the health care system of Turkey. [ABSTRACT FROM AUTHOR]

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]

Stereopsis of X-ray images can produce 3D tree of coronary arteries up to a certain accuracy level with a lower dose of radiation when compared to computer tomography (CT). In this study, a novel and complete automatic system is designed that covers preprocessing, segmentation, matching and reconstruction steps for that purpose. First, an automatic and novel pattern recognition technique is applied for extraction of the bifurcation points with their diameters recorded in a map. Then, a novel optimization algorithm is run for matching the branches efficiently which is based on that map and the epipolar geometry of stereopsis. Finally, cut branches are fixed one by one at the bifurcations for completing the 3D reconstruction. This method prevails the similar ones in the literature with this novelty since it automatically and inherently prevents the wrong overlapping of branches. Other essential problems like correct detection of the bifurcations and accurate calibration parameters and fast overlapping of matched branches are addressed at acceptable levels. The accuracy of bifurcation extraction is high at 90 % with 96 % sensitivity. Accuracy of vessel centerlines has rootmean-square (rms) error smaller than 0.57 mm for 20 different patients. For phantom model, rms error is 0.75 ± 0.8 mm in 3D localization. [ABSTRACT FROM AUTHOR]

Neonatal jaundice is a common condition that occurs in newborn infants in the first week of life. Today, techniques used for detection are required blood samples and other clinical testing with special equipment. The aim of this study is creating a non-invasive system to control and to detect the jaundice periodically and helping doctors for early diagnosis. In this work, first, a patient group which is consisted from jaundiced babies and a control group which is consisted from healthy babies are prepared, then between 24 and 48 h after birth, 40 jaundiced and 40 healthy newborns are chosen. Second, advanced image processing techniques are used on the images which are taken with a standard smartphone and the color calibration card. Segmentation, pixel similarity and white balancing methods are used as image processing techniques and RGB values and pixels' important information are obtained exactly. Third, during feature extraction stage, with using colormap transformations and feature calculation, comparisons are done in RGB plane between color change values and the 8-color calibration card which is specially designed. Finally, in the bilirubin level estimation stage, kNN and SVR machine learning regressions are used on the dataset which are obtained from feature extraction. At the end of the process, when the control group is based on for comparisons, jaundice is succesfully detected for 40 jaundiced infants and the success rate is 85 %. Obtained bilirubin estimation results are consisted with bilirubin results which are obtained from the standard blood test and the compliance rate is 85 %. [ABSTRACT FROM AUTHOR]

Using of EMR in health services and organizations is steadily increasing for quality improvement, cost effectiveness and performance development. However, no validated national and international instruments (scale, questionnaire, index, and inventory) have assessed the effectiveness, satisfaction, health care savings, patient safety and cost minimization of electronic medical and health systems from the viewpoint and perceptions of nurses in Turkish health services. The perceptions of health care professionals especially physicians and nurses can contribute important information that may predict their acceptance of EMR and desired mode of use for EMR, evaluation performance of EMR thus guiding EMR implementation in hospitals. This article is a report of validation of the instrument to measure nurses' views on the use, quality and user satisfaction with EMR in Turkish health system. Items in the questionnaire were designed and obtained following O.G. Otieno, H. Toyama, M. Asonuma, M. Kanai-Pak, K. Naitoh's questionnaire about Use, Quality and User Satisfaction with EMR systems. Reliability and validity were examined and investigated in terms of responses from 487 nurses from one education hospital in Ankara, Turkey. This study was planned and conducted at a university hospital. The validation process was based on construct validity in this study. The response rate was 74.92 %. Cronbach's alphas of three factors (use, quality and satisfaction of EMR) ranged from 0.78 to 0.94. Goodness-of-fit indices from the confirmatory factor analysis showed a reasonable model fit. Results of confirmatory factor analysis showed that χ statistic indicated significant result ( p < 0.001) and model fit was acceptable according to relative χ statistic (χ/df = 2.8 < 5). Further validation of the instrument could yield positive results in health systems in the different countries. Also further validation and reliability studies could be planned on physicians and other health professionals. [ABSTRACT FROM AUTHOR]

This study was based on knowledge sharing barriers about attitudes of physicians in Turkish health care system. The present study aims to determine whether the knowledge sharing barriers about attitudes of physicians vary depending on gender, position, departments at hospitals, and hospital ownership status. This study was planned and conducted on physicians at one public hospital, one university hospital, and one private hospital in Turkey. 209 physicians were reached for data collection. The study was conducted in June-September 2014. The questionnaire (developed by A. Riege, (J. Knowl. Manag. 9(3):18-35, )), five point Likert-type scale including 39 items having the potential of the physicians' knowledge- sharing attitudes and behaviors, was used in the study for data collection. Descriptive statistics, reliability analysis, student t test and ANOVA were used for data analysis. According to results of this study, there was medium level of knowledge sharing barriers within hospitals. In general, physicians had perceptions about the lowest level individual barriers, intermediate level organizational barriers and the highest level technological barriers perceptions, respectively. This study revealed that some knowledge sharing barriers about attitudes of physicians were significantly difference according to hospital ownership status, gender, position and departments. Most evidence medical decisions and evidence based practice depend on experience and knowledge of existing options and knowledge sharing in health care organizations. Physicians are knowledge and information-intensive and principal professional group in health care context. [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 study which examined the multi-item dimensionality of patients' perceived value in hospital service. The study was carried out at the Gulhane Military Medical Academy (GMMA) in Ankara, Turkey in August 2008. The perceived values are functional value, emotional value, and social value.

The article presents a study which examined the preferences of patients in remote health monitoring, particularly among diabetes and obesity patients in Istanbul, Turkey. Also cited are the other new health care services like telemedicine and telecare, as well as the study which identified the various types of telehealth adopters like positive user, hesitant user, and positive participant.

The article presents a study on the views of nurses on the use of electronic medical records (EMR) in Turkey in terms of use, quality and user satisfaction. Also examined are the relationships among the use, quality and user satisfaction of EMR, as well as the mean quality scores for the EMR systems in the Ministry of health hospital, the university hospital, and the private hospital.

The article discusses a study which examined the daily and hourly donor arrival patterns at Hacettepe University Hospitals' Blood Center using the Two-Step Cluster Method and the Classification and Regression Trees method. Topics covered include the nonconstant donor arrival rates experienced by blood centers, the use of a serial queuing network model of the donation process and the processes for improving blood collection services.

The article presents a study on a pilot test course for technicians in intensive care unit at Medical Devices Repairing and Maintenance Center (TABOM) of Turkish High Specialized Hospital in Ankara, Turkey. The 15-hour moodle course management system is a web based learning of 3D medical equipment models used in hospital intensive care unit. It is suggested that the course enhances and supports the interactive learning environment.

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 article presents the spectral analysis of the electroencephalogram (EEG) signals in migraine and epileptic patients through visual evoked potentials (VEP). Topics discussed include the light hypersensitivity of migraine patients, the visual inspection of power spectral densities as well as the Burg AR parametric method of EEG signal analysis. Also mentioned are flash stimuli at different frequencies, neurological attacks and migraine/epileptic seizure detection.

Tuberculosis is an infectious disease, caused in most cases by microorganisms called Mycobacterium tuberculosis. Tuberculosis is a great problem in most low income countries; it is the single most frequent cause of death in individuals aged fifteen to forty-nine years. Tuberculosis is important health problem in Turkey also. In this study, a study on tuberculosis diagnosis was realized by using multilayer neural networks (MLNN). For this purpose, two different MLNN structures were used. One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. A general regression neural network (GRNN) was also performed to realize tuberculosis diagnosis for the comparison. Levenberg-Marquardt algorithms were used for the training of the multilayer neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on tuberculosis diseases diagnosis. The tuberculosis dataset were taken from a state hospital’s database using patient’s epicrisis reports. [ABSTRACT FROM AUTHOR]

Influenza pandemics have occurred intermittently throughout the 20th century and killed millions of people worldwide. It is expected that influenza pandemics will continue to occur in the near future. Huge number of deaths and cases is the most troublesome aspect of the influenza pandemics, but the other important trouble is the economic impact of the influenza pandemics to the countries. In this study, we try to detect the cost of a possible influenza pandemic under different scenarios and attack rates. We include the vaccination and antiviral treatment cost for direct cost and we add the work absenteeism cost to the calculations for indirect cost of influenza pandemics. As a case study, we calculate the economic impact of pandemic influenza for Turkey under three different scenarios and three different attack rates. Our optimistic estimation shows that the economic impact of pandemic influenza will be between 1.364 billion dollars and 2.687 billions dollars to Turkish economy depending on the vaccination strategies. [ABSTRACT FROM AUTHOR]

Pandemic influenza has been considered as a serious international health risk by many health authorities in the world. In mitigating pandemic influenza, effective allocation of limited health resources also plays a critical role along with effective use of medical prevention and treatment procedures. A national resource allocation program for prevention and treatment must be supported with the right allocation decisions for all regions and population risk groups. In this study, we develop a multi-objective mathematical programming model for optimal resource allocation decisions in a country where a serious risk of pandemic influenza may exist. These resources include monetary budget for antivirals and preventive vaccinations, Intensive Care Unit (ICU) beds, ventilators, and non-Intensive Care Unit (non-ICU) beds. The mathematical model has three objectives: minimization of number of deaths, number of cases and total morbidity days during a pandemic influenza. This model can be used as a decision support tool by decision makers to assess the impact of different scenarios such as attack rates, hospitalization and death ratios. These factors are found to be very influential on the allocation of the total budget among preventive vaccination, antiviral treatment and fixed resources. The data set collected from various sources for Turkey is used and analyzed in detail as a case study. [ABSTRACT FROM AUTHOR]

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]

This study investigates the effects of work context on professional job processes in large organizations in general, and the differences of perceptions and expectations of physicians for professional autonomy in different work environments in specific. An empirical study was conducted on “perceptions and expectations of physicians” in terms of their control over diagnosis and treatment process in different teaching hospitals in Turkey. Results exhibit that the difference between expectation and perception on “the control over diagnosis and treatment process”, one of the crucial elements in professional autonomy of physicians, is statistically significant among the surveyed hospitals. The reason of this difference seems to be the managerial practices of each organization. Thus, the study emphasizes the need of assessing the problematic job procedures and of changing them with the most effective ones to create a synergy instead of conflict in the work setting. This synergy in the health care organizations means a balance between managerial control and professional autonomy and a better health service to the patient. [ABSTRACT FROM AUTHOR]

The purpose of this study was to compare three alternative surgical therapies for Benign prostatic hyperplasia (BPH) in terms of their cost and effectiveness in a general hospital setting in Turkey. BPH is an important potential burden on health care resources in developing countries like Turkey, not only because of the increasing incidence of the disease, but also because of the differences in cost and effectiveness that exist among the various available treatment technologies. In this study, three alternative surgical treatments for BPH, namely open prostatectomy, transurethral resection of the prostate (TURP), and laser prostatectomy were compared in terms of their cost and effectiveness. Effectiveness was measured through the use of the International Prostate Symptom Score (IPSS) before the operation and at 3 months after. Laser prostatectomy was found to be the most costly and least effective way of treating BPH. This conclusion was valid both in terms of cost per prostate symptom score and cost per improvement of quality of life index. This study shows how a rough estimate can be made of the potential burdens that alternative treatment methods would impose on a country’s health budget. [ABSTRACT FROM AUTHOR]

Patient satisfaction is an important measure of service quality in health care systems. Patients' perceptions about health care systems seem to have been largely ignored by health care managers in developing countries. The aim of this study is to develop a reliable and valid instrument to measure patient satisfaction in Turkey. A questionnaire was developed and a total of 1100 patients in 31 different hospitals were interviewed. Factor analysis was utilized to determine the factor structure. The instrument of the patient satisfaction developed in this study provides insights to the researches who study the improvement of patient satisfaction with service quality of hospitals, practitioners, and the decision makers. [ABSTRACT FROM AUTHOR]

This article describes methods used to determine the severity of Dry Eye Syndrome (DES) based on Oxford Grading Schema (OGS) automatically by developing and applying a decider model. The number of dry punctate dots occurred on corneal surface after corneal fluorescein staining can be used as a diagnostic indicator of DES severity according to OGS; however, grading of DES severity exactly by carefully assessing these dots is a rather difficult task for humans. Taking into account that current methods are also subjectively dependent on the perception of the ophtalmologists coupled with the time and resource intensive requirements, enhanced diagnosis techniques would greatly contribute to clinical assessment of DES. Automated grading system proposed in this study utilizes image processing methods in order to provide more objective and reliable diagnostic results for DES. A total of 70 fluorescein-stained cornea images from 20 patients with mild, moderate, or severe DES (labeled by an ophthalmologist in the Keratoconus Center of Yildirim Beyazit University Ataturk Training and Research Hospital) used as the participants for the study. Correlations between the number of dry punctate dots and DES severity levels were determined. When automatically created scores and clinical scores were compared, the following measures were observed: Pearson’s correlation value between the two was 0.981; Lin’s Concordance Correlation Coefficients (CCC) was 0.980; and 95% confidence interval limites were 0.963 and 0.989. The automated DES grade was estimated from the regression fit and accordingly the unknown grade is calculated with the following formula: Gpred = 1.3244 log(Ndots) - 0.0612. The study has shown the viability and the utility of a highly successful automated DES diagnostic system based on OGS, which can be developed by working on the fluorescein-stained cornea images. Proper implemention of a computationally savvy and highly accurate classification system, can assist investigators to perform more objective and faster DES diagnoses in real-world scenerios. [ABSTRACT FROM AUTHOR]

Low back pain (LBP) is one of the common problems encountered in medical applications. This paper proposes two expert systems (artificial neural network and adaptive neuro-fuzzy inference system) for the assessment of the LBP level objectively. The skin resistance and visual analog scale (VAS) values have been accepted as the input variables for the developed systems. The results showed that the expert systems behave very similar to real data and that use of the expert systems can be used to successfully diagnose the back pain intensity. The suggested systems were found to be advantageous approaches in addition to existing unbiased approaches. So far as the authors are aware, this is the first attempt of using the two expert systems achieving very good performance in a real application. In light of some of the limitations of this study, we also identify and discuss several areas that need continued investigation.

In this paper, we present MedWise, a high level design of a medical information infrastructure, and its architecture. The proposed system offers a comprehensive, modular, robust and extensible infrastructure to be used in public health care systems. The system gathers reliable and evidence based health data, which it then classifies, interprets and stores into a particular database. It creates a healthcare ecosystem that aids the medical community by providing for less error prone diagnoses and treatment of diseases. This system will be standards-compliant; therefore it would be complementary to the existing healthcare and clinical information systems. The key objective of the proposed system is to provide as much medical historical and miscellaneous data as possible about the patients with minimal consultation, thus allowing physicians to easily access Patients' Ancillary Data (PAD) such as hereditary, residential, travel, custom, meteorological, biographical and demographical data before the consultation. In addition, the system can help to diminish problems and misdiagnosis situations caused by language barriers-disorders and misinformation. MedWise can assist physicians to shorten time for diagnosis and consultations, therefore dramatically improving quality and quantity of the physical examinations of patients. Furthermore, since it intends to supply a significant amount of data, it may be used to improve skills of students in medical education.

Breast cancer continues to be a significant public health problem in the world. The diagnosing mammography method is the most effective technology for early detection of the breast cancer. However, in some cases, it is difficult for radiologists to detect the typical diagnostic signs, such as masses and microcalcifications on the mammograms. This paper describes a new method for mammographic image enhancement and denoising based on wavelet transform and homomorphic filtering. The mammograms are acquired from the Faculty of Medicine of the University of Akdeniz and the University of Istanbul in Turkey. Firstly wavelet transform of the mammograms is obtained and the approximation coefficients are filtered by homomorphic filter. Then the detail coefficients of the wavelet associated with noise and edges are modeled by Gaussian and Laplacian variables, respectively. The considered coefficients are compressed and enhanced using these variables with a shrinkage function. Finally using a proposed adaptive thresholding the fine details of the mammograms are retained and the noise is suppressed. The preliminary results of our work indicate that this method provides much more visibility for the suspicious regions.

The objective of this paper is to demonstrate the utility of artificial neural networks, in combination with wavelet transforms for the detection of mammogram masses as malign or benign. A total of 45 patients who had breast masses in their mammography were enrolled in the study. The neural network was trained on the wavelet based feature vectors extracted from the mammogram masses for both benign and malign data. Therefore, in this study, Multilayer ANN was trained with the Backpropagation, Conjugate Gradient and Levenberg-Marquardt algorithms and ten-fold cross validation procedure was used. A satisfying sensitivity percentage of 89.2% was achieved with Levenberg-Marquardt algorithm. Since, this algorithm combines the best features of the Gauss-Newton technique and the other steepest-descent algorithms and thus it reaches desired results very fast.

It is very important to identify the appropriate donor in organ transplantation under the time constraint. Clearly, adequate time must be spent in appropriate donor research in that kind of vital operation. On the other hand, time is very important to search for other alternatives in case of inappropriate donor. However, the possibility for determining the most probable donors as fast as possible has an great importance in using time efficiently. From this point view, the main objective of this paper is developing a system which provides probabilistic prior information in donor transplantation via data mining. While the sytem development process, the basic element is the data of successful organ transplantations. Then, the hidden information and patterns will be discovered from this data. Therefore, this process requires the data mining methods from its definition. In this study, an appropriate donor detection system design based on data mining is suggested.

Emergency medical services (EMS) play a crucial role in the overall quality and performance of health services. The performance of these systems heavily depends on operational success of emergency services in which emergency vehicles, medical personnel and supporting equipment and facilities are the main resources. Optimally locating and sizing of such services is an important task to enhance the responsiveness and the utilization of limited resources. In this study, an integer optimization model is presented to decide locations and types of service stations, regions covered by these stations under service constraints in order to minimize the total cost of the overall system. The model can produce optimal solutions within a reasonable time for large cities having up to 130 districts or regions. This model is tested for the EMS system of Adana metropolitan area in Turkey. Case study and computational findings of the model are discussed in detail in the paper.

A new rule based fuzzy filter for removal of highly impulse noise, called Rule Based Fuzzy Adaptive Median (RBFAM) Filter, is aimed to be discussed in this paper. The RBFAM filter is an improved version of Adaptive Median Filter (AMF) and is presented in the aim of noise reduction of images corrupted with additive impulse noise. The filter has three stages. Two of those stages are fuzzy rule based and last stage is based on standard median and adaptive median filter. The proposed filter can preserve image details better then AMF while suppressing additive salt & pepper or impulse type noise. In this paper, we placed our preference on bell-shaped membership function instead of triangular membership function in order to observe better results. Experimental results indicates that the proposed filter is improvable with increased fuzzy rules to reduce more noise corrupted images and to remove salt and pepper noise in a more effective way than what AMF filter does.

This paper illustrates the use of combined neural network (CNN) models to guide model selection for diagnosis of internal carotid arterial (ICA) disorders. The ICA Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first level networks were implemented for the diagnosis of ICA disorders using the statistical features as inputs. To improve diagnostic accuracy, the second level network was trained using the outputs of the first level networks as input data. The CNN models achieved accuracy rates which were higher than that of the stand-alone neural network models.