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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]

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]

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]