The effect of stroboscopic training on ankle mechanics during gait in chronic ankle instability: Clinical trials

Objective: To determine the effect of 6-week stroboscopic training on ankle gait mechanics in athletes with chronic ankle instability. Material and Methods: Thirty-nine participants were assigned to the stroboscopic group (SG, n=13), non-stroboscopic group (NSG, n=13), and control group (CG, n=13). Three-dimensional kinematic pretest gait analysis was performed with the Noraxon system. Ankle joint angles were recorded for 75 seconds while the athletes walked on a treadmill at a speed of 3.5 m/s. After the pretest, the SG performed 6 weeks of balance training with stroboscopic vision, the NSG performed 6 weeks of balance training without stroboscopic vision, and the CG received no training. Ankle gait analysis was repeated after 6 weeks. Repeated-measures analysis of variance with one between-subjects factor was performed. Results: Gait analysis revealed a significant increase in ankle dorsiflexion angle between pretest and posttest in the SG (p<0.001, ηp2=0.34). Between-group comparisons showed significantly higher dorsiflexion angle in the SG compared to the CG (p=0.001, ηp2=0.15) and NSG (p=0.002, ηp2=0.11). Gait analysis of 100 kinematic data points starting at heel strike was performed using MATLAB. The results demonstrated the increase in ankle range of motion in the SG occurred in the dorsiflexion angle during the midstance phase of gait. Conclusion: Stroboscopic glasses modulate visual feedback and may be clinically useful in allowing progressive rehabilitationtargeting the dependence on visual feedback for motor control

Kısa dönem öngörü için GMDH türünde sinir ağı algoritmaları.

Group Method of Data Handling (GMDH) - type neural network algorithms are the heuristic self-organization method for modelling the complex systems. GMDH algorithms are utilized for the variety of purposes, which are identification of physical laws, extrapolation of physical fields, pattern recognition, clustering, approximation of multidimensional processes, forecasting without models and so on. In this study, GMDH - type neural network algorithms were applied to make forecasts for time series data sets. We mainly focused on development of free software. For this purpose, we developed an R package GMDH. Moreover, we integrated different transfer functions, sigmoid, radial basis, polynomial, and tangent functions, into GMDH algorithm. We proposed an algorithm in which all transfer functions are used simultaneously or separately if desired. Also, we used regularized least square estimation for the estimation of weights to overcome multi-collinearity problem. The methods were illustrated on real life datasets having different properties to see the prediction and forecasting performance of the algorithm. We included ARIMA models and exponential smoothing methods for the comparison purpose. GMDH algorithms show the same or even better performance than the other methods.M.S. - Master of Scienc

Binary Classification via GMDH-Type Neural Network Algorithm

Group Method of Data Handling (GMDH) - type neural network algorithms are the self organizing algorithms for modeling complex systems. GMDH algorithms are used for different objectives; examples include regression, classification, clustering, forecasting, and so on. In this thesis, we propose a new algorithm named as diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm for binary classification. Also, we develop an R package, GMDH2, to make our proposed algorithm available. The package offers two main algorithms, GMDH and dce-GMDH algorithms. GMDH algorithm performs binary classification and returns important variables. dce-GMDH algorithm performs binary classification by assembling classifiers based on GMDH algorithm. The package also provides a well-formatted table of descriptives in different format (R, LaTeX, HTML). Moreover, it produces confusion matrix and related statistics, and interactive scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. All properties of the package are demonstrated on Wisconsin Breast Cancer data. A Monte Carlo simulation study is also conducted to compare GMDH algorithms to the other well-known classifiers under the different conditions. Moreover, a user-friendly web-interface of the package is developed especially for non-R users. This web-interface is available at http://www.softmed.hacettepe.edu.tr/GMDH2.Veri işleme grup yöntemi (GMDH) türünde sinir ağı algoritmaları karmaşık sistemleri modellemeye yarayan kendi kendini organize eden yöntemlerdir. GMDH algoritmaları regresyon, sınıflandırma, kümeleme, öngörü gibi çeşitli amaçlar için kullanılmaktadır. Bu tez kapsamında GMDH temelli farklı sınıflandırıcıların birleştirilmesi (dce-GMDH) adında yeni bir algoritma önerilmektedir. Bu algoritmaya ulaşılabilmesi için GMDH2 adında bir R paketi geliştirilmiştir. Paket GMDH ve dce-GMDH adında iki temel algoritma sunmaktadır. GMDH algoritması ikili sınıflandırma yapmakta ve önemli değişkenleri bulmaktadır. dce-GMDH algoritması ise farklı sınıflandırıcıları GMDH temelli olarak birleştirerek ikili sınıflandırma yapmaktadır. Paket farklı formatlarda (R, LaTeX, HTML) tanımlayıcı istatistiklerin tablosunu üretmektedir. Ek olarak, paket sınıflandırma performansı değerlendirmek amacıyla karışıklık matrisi, ilgili istatistikleri ve sınıflandırma etiketleri ile birlikte etkileşimli saçılım grafiği (2 ve 3 boyutlu) üretmektedir. Paketin tüm özellikleri Wisconsin meme kanseri verisi ile sunulmaktadır. GMDH algoritmaları ile diğer iyi bilinen sınıflandırıcıları karşılaştırmak amacıyla Monte Carlo benzetim çalışması yapılmıştır. R kullanıcısı olmayanlar için paketin kullanıcı dostu bir web uygulaması geliştirilmiştir. Bu web uygulaması http://www.softmed.hacettepe.edu.tr/GMDH2 adresi ile kullanıma açılmıştır

Asymmetric Confidence Interval with Box-Cox Transformation in R

Normal distribution is important in statistical literature since most of the statistical methods are based on normal distribution such as t-test, analysis of variance and regression analysis. However, it is difficult to satisfy the normality assumption for real life datasets. Box–Cox power transformation is the most well-known and commonly utilized remedy [2]. The algorithm relies on a single transformation parameter. In the original article [2], maximum likelihood estimation was proposed for the estimation of transformation parameter. There are other algorithms to obtain transformation parameter. Some of them include the studies of [1], [3] and [4]. Box– Cox power transformation is given by = { −1 , ≠ 0 , = 0 . Here, is the power transformation parameter to be estimated, ’s are the observed data, ’s are transformed data. In this study, we focus on obtaining the mean of data and a confidence interval for it when Box-Cox transformation is applied. Since the transformation is applied, the scale of the data has changed. Therefore, reporting the mean and confidence interval obtained from transformed data is not meaningful for the researchers. Besides, reporting mean and symmetric confidence interval obtained from original data becomes misleading for the researchers since the normality assumption is not satisfied. Therefore, it is pointed out that mean and asymmetric confidence interval obtained from back transformed data must be reported. We have written down a generic function to obtain the mean of data and a confidence interval for it when Box-Cox transformation is applied. It is released under R package AID with the name of “confInt” for implementation

Estimating Box-Cox power transformation parameter via goodness-of-fit tests

Box-Cox power transformation is a commonly used methodology to transform the distribution of the data into a normal distribution. The methodology relies on a single transformation parameter. In this study, we focus on the estimation of this parameter. For this purpose, we employ seven popular goodness-of-fit tests for normality, namely Shapiro-Wilk, Anderson-Darling, Cramer-von Mises, Pearson Chi-square, Shapiro-Francia, Lilliefors and Jarque-Bera tests, together with a searching algorithm. The searching algorithm is based on finding the argument of the minimum or maximum depending on the test, i.e., maximum for the Shapiro-Wilk and Shapiro-Francia, minimum for the rest. The artificial covariate method of Dag etal. (2014) is also included for comparison purposes. Simulation studies are implemented to compare the performances of the methods. Results show that Shapiro-Wilk and the artificial covariate method are more effective than the others and Pearson Chi-square is the worst performing method. The methods are also applied to two real-life datasets. The R package AID is proposed for implementation of the aforementioned methods

GMDH An R Package for Short Term Forecasting Via GMDH Type Neural Network Algorithms

Group Method of Data Handling (GMDH)-type neural network algorithms are the heuristic self organization method for the modelling of complex systems. GMDH algorithms are utilized for a variety of purposes, examples include identification of physical laws, the extrapolation of physical fields, pattern recognition, clustering, the approximation of multidimensional processes, forecasting without models, etc. In this study, the R package GMDH is presented to make short term forecasting through GMDH-type neural network algorithms. The GMDH package has options to use different transfer functions (sigmoid, radial basis, polynomial, and tangent functions) simultaneously or separately. Data on cancer death rate of Pennsylvania from 1930 to 2000 are used to illustrate the features of the GMDH package. The results based on ARIMA models and exponential smoothing methods are included for comparison

PİSİDİA BÖLGESİ HASTANELERİNDE KALİTE VE SAĞLIK TURİZMİ ETKİLERİNİN DEĞERLENDİRİLMESİ

Sağlık turizmi hastaları tedavi olmak için kendi ülkelerinden başka ülkelere seyahat etmek suretiyle şifa aramasına denilmektedir. Son dönemlerde yurtdışından çok sayıda turistin sağlık turizmi kapsamında ülkemize tedavi olmak amacıyla ziyarette bulunmaktadır. Ülkemizde sağlık turizmi hizmeti veren şehirlerin sayısının arttırılması ülke ve bölge ekonomisine olumlu katkı sağlayacaktır. Pisidia bölgesi tarihi ve doğal güzellikler açısından turizmde ilgi odağı olan bölgedir. Turizm alanlarının çeşitlendirilmesi açısından bölgede sağlık turizmi hizmetlerinin yaygınlaştırılması yurtdışından çok sayıda ziyaretçinin bölgeyi tercih etmesine sebep olacaktır. Bu çalışmada, Pisidia bölgesi şehirleri olan Burdur Isparta İllerinde sağlık turizmi açısından hastanelerin kalite ölçeklerinin değerlendirilmesi ve sağlık turizminin etkilerine yönelik tutumun değerlendirilmesine yöneliktir. Çalışmanın amacı bölgede sağlık turizmine yönelik hizmetlerin verilmesinin ekonomik, sosyo-kültürel ve çevresel etkilerini değerlendirmeyi amaçlamaktadır. Sonuç olarak bölgedeki hastanelerin kalite ve sağlık turizmi etkileri değerlendirildiğinde, sağlık turizminin yaygınlaşmasının bölgeye ekonomik, sosyo-kültürel ve çevresel etkiler anlamında olumlu katkı sağlayacağı ortaya çıkmıştır. Yapılan araştırmada kent hastaneleri kalitesinin sağlık turizmine; ekonomik, sosyo-kültürel ve çevresel faktörlerin olumlu etkileri üzerine kurulan hipotezler desteklenmiştir. Kent hastaneleri kalitesinin sağlık turizmine; ekonomik, sosyo-kültürel ve çevresel faktörlerin olumsuz etkileri üzerine kurulan hipotezler desteklenmemiştirIn recent years, health tourism has been called patients seeking healing by traveling from their own countries to other countries for treatment. Recently, many tourists from abroad have been visiting our country for treatment within the scope of health tourism. Increasing the number of cities providing health tourism services in our country will contribute positively to the economy of the country and the region. Pisidia region is the center of interest in tourism in terms of historical and natural beauties. The expansion of health tourism services in the region in terms of diversification of tourism areas will cause many visitors from abroad to prefer the region In this study, it is aimed to evaluate the quality scales of hospitals in terms of health tourism in Burdur Isparta provinces, which are cities of Pisidia region, and to evaluate the attitude towards the effects of health tourism. The aim of the study is to evaluate the economic, socio-cultural and environmental effects of health tourism services in the region. As a result, when the quality and health tourism impacts of the hospitals in the region are evaluated, it has been revealed&nbsp;</p