Plazma glukoz konsantrasyonu, serum insülin direnci ve diastolik kan basıncı göstergeleri ile makine öğrenme yöntemleri kullanılarak diyabet hastalığının erken tanısı

Aim: It is a known fact that diabetes mellitus is increasing frequently and triggering many different diseases. Therefore, early diagnosis of the disease is important. This study was trying to predict the early diagnosis of the disease, according to machine learning methods by measuring plasma glucose concentration, serum insulin resistance, and diastolic blood pressure. Material and Methods: In the study, the public dataset from a website consists of 768 samples and nine variables. Three different machine learning strategies were used in the early diagnosis of diabetes mellitus (Support Vector Machine, Multilayer Perceptron, and Stochastic Gradient Boosting). 3 repeats and 10 fold cross-validation method was used to optimize the hyperparameters. The model’s performance parameters were evaluated based on accuracy, specificity, sensitivity, confusion matrix, positive predictive value (precision), negative predictive value, and AUC (area under the ROC curve). Results: According to the experimental results (the criteria of accuracy (0.79), sensitivity (0.57), specificity (0.91), positive predictive value (0.79), negative predictive value (0.80), and AUC (0.74)) the Support Vector Machine was more successful than other methods. Conclusion: Plasma glucose concentration, serum insulin resistance, and diastolic blood pressure markers are important indicators in the early diagnosis of diabetes mellitus. In this study, it was seen that these markers make a significant contribution to the early diagnosis of diabetes mellitus. However, it has been observed that these indicators alone will not be sufficient in the early diagnosis of the disease, especially since age, body mass index and pregnancy contribute significantly.Amaç: Diyabetin sıklıkla arttığı ve bir çok farklı hastalığı tetiklediği bilinen bir gerçektir. Bu nedenle hastalığın erken teşhisi önemlidir. Bu çalışmada plazma glukoz konsantrasyonu, serum insülin direnci ve diyastolik kan basıncı göstergelerinden, makine öğrenmesi yöntemlerine göre hastalığın erken teşhisi öngörülmeye çalışılmıştır. Materyal ve Metot: Çalışmada, bir web sitesinden alınan halka açık veri seti 768 örnek ve dokuz değişkenden oluşmaktadır. Diyabetin erken teşhisinde üç farklı makine öğrenme stratejisi kullanıldı (Destek Vektör Makineleri, Çok Katmanlı Algılayıcılar ve Stokastik Gradyan Artırma). Hiper parametre optimizasyonu için 3 tekrarlı 10 kat tekrarlı çapraz doğrulama yöntemi kullanıldı. Modellerin performansı doğruluk, seçicilik, duyarlılık, karışıklık matrisi, pozitif tahmin değeri (kesinlik), negatif tahmin değeri ve AUC (ROC eğrisi altında kalan alan) temel alınarak değerlendirilmiştir. Bulgular: Deneysel sonuçlara göre (doğruluk (0.79), duyarlılık (0.57), özgüllük (0.91), pozitif tahmin değeri (0.79), negatif tahmin değeri (0.80) ve AUC (0.74) kriterleri), Destek Vektör Makineleri diğer yöntemlere göre daha başarılı çıkmıştır. Sonuç: Diyabet hastalığının erken tanısında plazma glukoz konsantrasyonu, serum insülin direnci ve diastolik kan basinci belirteçleri önemli göstergelerdir. Bu çalışmada da bu belirteçlerin diyabetin erken tanısında önemli katkı sağladığı görülmüştür. Ancak tek başlarına bu göstergelerin hastalığın erken tanısında yeterli olmayacağı özellikle yaş, beden kitle indeksi ve gebeliğin de önemli derecede katkı sağladığı görülmüştür

An investigation of ensemble learning methods in classification problems and an application on non-small-cell lung cancer data

This study aims to classify NSCLC death status and consists of patient records of 24 variables created by the open-source dataset of the cancer data site. Besides, basic classifiers such as SMO (Sequential Minimal Optimization), K-NN (K-Nearest Neighbor), random forest, and XGBoost (Extreme Gradient Boosting), which are machine learning methods, and their performances, and voting, bagging, boosting, and stacking methods from ensemble learning methods were used. Performance evaluation of models was compared in terms of accuracy, specificity, sensitivity, precision, and Roc curve. The basic classifier performances of random forest, SMO, K-NN, and XGBoost classifiers, their performances in the bagging ensemble learning method, and their performances in the boosting ensemble learning method are evaluated. In addition, Model 1 (random forest + SMO), Model 2 (XGBoost + K-NN), Model 3 (random forest + K-NN), Model 4 (XGBoost+SMO), Model 5 (SMO+K-NN + random forest), Model 6 (SMO+K-NN+XGBoost) and Model 7 (SMO+K-NN + random forest + XGBoost) the performances of in different metrics were expressed. The boosting ensemble learning method, which provides the maximum classification performance with XGBoost, achieved a 0.982 accuracy value, 0.971 sensitivity value, 0.989 precision value, 0.989 specificity value, and 0.998 ROC curve. It is recommended to use ensemble learning methods for classification problems in patients with a high prevalence of cancer to achieve successful results

Türkiye’de kentleşme süreci ve illerin gsyh verileri ile göç oranları arasındaki ilişkinin kümeleme analiziyle incelenmesi

Migration is a concept as old as human history. Large-scale migrations have occurred in every period of human history. There are many economic, social, political and legal reasons behind migration movements. It is known that economic reasons are the most determining factors in the occurrence of migration. Generally, the direction of migration is from rural to urban, underdeveloped regions to developed regions, east to west in Turkey. Population is largely concentrated in cities. Cities with a high level of economic development are the cities with the most populous population. While many socio-economic indicators can be used to reveal the level of economic development, an evaluation can also be made based on the GDP data of the provinces, which is considered as a combination of these indicators. In the study, a statistical significance relationship is sought between migration data and GDP data of the provinces. The analysis method used is the cluster analysis. According to the results, the basic hypothesis of the study is confirmed at a rate of 79%. That means, the provinces with high GDP levels receive immigration, while the provinces with low GDP emigrate.Göç, insanlık tarihi kadar eski bir kavramdır. İnsanlık tarihinin her döneminde büyük ölçekli göçler meydana gelmiştir. Göç hareketlerinin temelinde çok sayıda ekonomik, sosyal, kültürel, siyasi ve hukuki neden yer almaktadır. Göçün ortaya çıkmasında en belirleyici etkenin ekonomik nedenler olduğu bilinmektedir. Türkiye’de göçün yönü genellikle kırdan kente, az gelişmiş yörelerden gelişmiş yörelere, doğudan batıya doğru olmuştur. Nüfus, büyük ölçüde kentlerde toplanmıştır. Ekonomik gelişmişlik düzeyi yüksek olan kentler, nüfusu en kalabalık olan kentlerdir. Ekonomik gelişmişlik düzeyini ortaya koymak üzere çok sayıda sosyo- ekonomik göstergeden yararlanılabileceği gibi, bu göstergelerin bir bileşkesi olarak kabul ettiğimiz illerin Gayrisafi Yurtiçi Hâsıla (GSYH) verilerini temel alarak bir değerlendirme de yapılabilir. Bu çalışmada, illerin göç verileri ile illerin GSYH verileri arasında istatistiksel bir anlamlılık ilişkisi aranmaktadır. Kullanılan analiz yöntemi kümeleme analizidir. Sonuçlara göre çalışmamızın temel hipotezi %79 oranında doğrulanmaktadır. Yani GSYİH düzeyi yüksek olan iller göç alırken, GSYİH düzeyi düşük olan iller göç vermektedir

Hydrogen Fuel Cell Powered Electric Vehicles And An Application Of Improvement For The Desorption Efficiency Of A Metal Hydride Storage

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2011Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2011İçinde bulunulan yüzyılda insanlarının en temel ihtiyaçlarından biri olan “ulaşım” yaygın olarak fosil yakıtlı araçlar tarafından karşılanmaktadır. Fosil yakıtların kıtlığı ve üretilmelerinden tüketilmelerine kadar olan çevrimde doğaya olan zararlı etkileri; alternatif, temiz enerji kaynaklarıyla (Ör: Güneş, Hidrojen) çalışan araçların gerekliliğini ortaya çıkarmaktadır. Bu çalışmada hidrojen yakıt hücreli elektrikli bir aracın, hidrojen saklama ortamının salıverme verimini arttırmak amaçlanmıştır. Aracın, polimer elektrolit membran tipli yakıt hücresi, hidrojen deposu olarak metal hidrid tüp grubu tarafından beslenmektedir. Araçta kullanılan metal hidrid saklama ortamlarının salıverme verimini arttırmak amacıyla kapalı devre ısı transfer sistemi kurulmuştur. Yapılan deneyler sonucunda kurulan sistemin, salınan hidrojen miktarını kayda değer oranda arttırdığı görülmüştür. Yakıt hücresi, çıkışında 800W güç üretecek şekilde yüklenmiş; ilk olarak ısı transfer sistemiyle, daha sonra ısı transfer sistemi olmadan, aynı şartlarda doldurulmuş metal hidrid tüpe bağlanmıştır. Isı transfer sistemi ile çalışan metal hidrid tüp 61 dakika boyunca; ısı transfer sistemi olmadan çalışan tüp ise sadece 18 dakika yakıt hücresini ortalama 800 W güç üretecek şekilde besleyebilmiştir.Fossil fuels are used by vehicles in transportation, which is one of the main needs of people. The share of transportation in fossil fuel usage is very high and daily oil consumption has been increasing. Shortage of fossil fuels and harmful effects’ have become main problem of today’s world. These causes force people to find new clean energy sources for vehicles to decrease pollutant gas emissions such as solar energy and hydrogen energy. In this study, it is aimed that improving the desorption efficiency of a hydrogen fuel cell vehicle’s hydrogen storage. The vehicle’s fuel cell type is PEM, which is sourced by a group of metal hydride hydrogen storage. To improve the desorption efficiency of the metal hydride storage; a closed-circuit heat transfer system was designed. The experiments showed that proposed system significantly increased the desorbed hydrogen amount from the metal hydride storage. The metal hydride storage was tested with and without proposed system while the PEM fuel cell was supplying 800 W output power. The storage could supply hydrogen for 61 minutes with proposed system, on the other hand; it could supply only 18 minutes without proposed system.Yüksek LisansM.Sc

The Social Media Addiction Among Turkish University Students

The objective of this study is to examine the place and importance of social media in the lives of university students according to several demographic variables. 323 Turkish students in total, 186 of whom were males and 137 of whom were females, studying in different departments at Selçuk University, participated in the study. A personal information form was used to obtain the socio-demographical information of the students, while “Social Media Addiction Scale” (SMAS), developed by Tutgun Ünal (2015), was implemented to determine the media addiction levels. Although no differences were observed concerning the age factor among the students; statistically significant differences were found among the averages of social media addiction with regards to sex, income, educational background of the parents, the means to access the internet, the number of years of access and the number of hours of access (p<0.05; p<0.01). Statistically significant variances were also found in all the dimensions of social media addiction concerning the time and the hours the students spend on social media (p<0.05; p<0.01). Based on the findings of this study, it can be said that the social media addiction levels of the male students are higher compared to that of the female students; this situation is caused by the social roles imposed on men and women depending on social status and responsibilities and the cultural structure; and as the number of days and hours spent on social media increase, so does the addiction to social media

Nutrition Knowledge and Attitude Change of Students Studying in State and Private Secondary Schools

The aim of this study is to analyse the changes in nutrition knowledge and attitudes of secondary school students depending on certain socio-demographic factors. The universe of the study is composed of 521 students, including 142 female and 379 male students studying in the secondary school and the sampling group in Konya province private and state central secondary education schools. The "Nutrition Knowledge and Attitude Scale", developed by Ertürk (2010), was used for nutrition attitude and knowledge and personal information form to acquire socio-demographic information. Descriptive statistics of the data were made, variance and homogeneity were tested, independent sample t test was used for binary comparisons, One Way Anowa was utilized for multiple comparisons, and Tukey test was benefitted to determine difference sources. Nutrition knowledge of students in state schools was found to be lower than that of students in private schools and this gap was identified to be statistically significant (P <0.05). Nutritional knowledge and attitudes of female students were determined to be higher than males’ and this difference was found to be statistically significant (P <0.05). Nutritional knowledge and attitudes of students who received elective nutrition classes were found to be higher than those who did not have nutrition classes and this change was again found to be statistically significant (P <0.05). The number of siblings and education status of parents were found to be statistically significant variants (P <0.05). As a result, the high level of nutrition knowledge and attitudes of female students compared to male students, the status and role of cultural transfer and social structure featured on male and female can be seen as the reason for that matter. It can be said that taking a nutrition class has a positive influence on nutrition knowledge and attitude, and that private school students have more nutrition knowledge yet similar nutrition attitudes with those in state schools

RSS-based wireless LAN indoor localization and tracking using deep architectures

Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73 m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35 m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2 m) under the same experiment environment.ECSEL Joint Undertaking ; European Union's H2020 Framework Programme (H2020/2014-2020) Grant ; National Authority TUBITA

THE EVALUATION OF THE CONNECTION BETWEEN MOTOR PERFORMANCE SKILLS AND BODY COMPOSITION OF THE GIRLS

The Purpose: This study was done for evaluating the connection between motor performance skills and body composition of the girls between 6-10 ages. The Method: 57 schoolgirls between 6-10 ages were included to the study. Weight, Age, Height, Body Mass Index (BMI), Free Fat Mass (FFM), Lean Muscle Mass (LMM), Body Fat Mass (BFM) and Body Fat Percentage (BFP) values were measured with (Inbody 230) branded tanita. Long jump, right hand and left hand strength, back strength, 20 meters speed test, sit &amp; reach test, 1 minute pull-up and flamingo balance tests were applied to the participants. Results: Pearson Correlation Test was applied for the analysis by using SPSS 23.0 program. According to the results, the average age is (8,00±,926) years, the average height is (128,7±6,64) cm and the average weight is (28,6±6,67) kg. There is a statistically meaningful relation between long jump, right hand, left hand, back strength, flamingo balance test and BFM, FFM, LMM, BMI and BFP at the (p&lt;0.01) level. However, there is no relation found between 20 meters speed test, sit &amp; reach test, 1 minute pull-up test and body composition parameters. Discussion and Conclusion: At the end of our study, we can say that there is a meaningful relation in a positive way between some motor performance skills and body composition of the girls.  Article visualizations

Gen verileri üzerinde ilginçlik ölçütleri kullanılarak birliktelik kuralları madenciliğinin uygulanması

Aim: Data mining is the discovery process of beneficial information, not revealed from large-scale data beforehand. One of the fields in which data mining is widely used is health. With data mining, the diagnosis and treatment of the disease and the risk factors affecting the disease can be determined quickly. Association rules are one of the data mining techniques. The aim of this study is to determine patient profiles by obtaining strong association rules with the apriori algorithm, which is one of the association rule algorithms. Material and Method: The data set used in the study consists of 205 acute myocardial infarction (AMI) patients. The patients have also carried the genotype of the FNDC5 (rs3480, rs726344, rs16835198) polymorphisms. Support and confidence measures are used to evaluate the rules obtained in the Apriori algorithm. The rules obtained by these measures are correct but not strong. Therefore, interest measures are used, besides two basic measures, with the aim of obtaining stronger rules. In this study For reaching stronger rules, interest measures lift, conviction, certainty factor, cosine, phi and mutual information are applied. Results: In this study, 108 rules were obtained. The proposed interest measures were implemented to reach stronger rules and as a result 29 of the rules were qualified as strong. Conclusion: As a result, stronger rules have been obtained with the use of interest measures in the clinical decision making process. Thanks to the strong rules obtained, it will facilitate the patient profile determination and clinical decision-making process of AMI patients.Amaç: Veri madenciliği, önceden büyük ölçekli verilerden ortaya çıkarılmayan faydalı bilgilerin keşfedilme sürecidir. Veri madenciliğinin yaygın olarak kullanıldığı alanlardan biri de sağlıktır. Veri madenciliği ile hastalığın tanı ve tedavisi ile hastalığı etkileyen risk faktörleri hızlı bir şekilde belirlenebilmektedir. Birliktelik kuralları, veri madenciliği tekniklerinden biridir. Bu çalışmanın amacı, birliktelik kuralı algoritmalarından biri olan apriori algoritması ile güçlü birliktelik kuralları elde ederek hasta profillerini belirlemektir. Materyal ve Metot: Çalışmada kullanılan veri seti 205 akut miyokard enfarktüsü (AMI) hastasından oluşmaktadır. Hastalar ayrıca FNDC5 polimorfizmlerinin rs3480, rs726344, rs16835198 genotipini de taşımaktadır. Apriori algoritması ile elde edilen kuralları değerlendirmek için destek ve güven ölçüleri kullanılır. Ancak bu ölçütler ile elde edilen kurallar doğrudur ancak güçlü değildir. Bu nedenle, daha güçlü kurallar elde etmek amacıyla iki temel ölçütün yanı sıra ilginçlik ölçütleri kullanılmaktadır. Bu çalışmada daha güçlü kurallara ulaşmak için ilginçlik ölçütlerinden kaldıraç, kanaat, kesinlik faktörü, cosine, korelasyon katsayısı (phi) ve karşılıklı bilgi ölçütleri uygulanmıştır. Bulgular: Çalışmada 108 kural elde edilmiştir. Bu kurallara ilginçlik ölçütlerinin de uygulanması ile elde edilen kural sayısı 29 olmuştur ve bu kurallar güçlü kural olarak nitelendirilmiştir. Sonuç: Sonuç olarak, klinik karar verme sürecinde ilginçlik ölçütlerinin kullanılmasıyla daha güçlü kurallar elde edilmiştir. Elde edilen güçlü kurallar sayesinde AMİ hastalarının hasta profili belirleme ve klinik karar verme sürecini kolaylaştıracaktır

Computed tomography-based morphometric measurements of the atlas (C1) posterior arc

Study design: Single-center retrospective study Objectives: This study is performed to determine the anatomic feasibility of the C1 posterior arc screw and help select an optimal screw trajectory in treating patients with craniovertebral junction pathologies. Material and Methods: We reported a single-centre retrospective study. Forty patients (20 male and 20 female) who underwent cervical computed tomography (CT) were chosen from the hospital records. Based on CT images, we measured left laminar length (LLL), right laminar length (RLL), left laminar angle (LLA), right laminar angle (RLA), left laminar axial thickness (LLAT), right laminar axial thickness (RLAT), left laminar coronal thickness (LLCT), right laminar coronal thickness (RLCT), and craniocaudal angle (CCA) of the C1 posterior arc. Results: The mean values and standard deviations (SD) for nine parameters at the C1 posterior arc were determined. LLL, RLL, LLCT, and RLCT were statistically longer in men than women. RLAT was bigger in men but there was no statistical difference. RLA was statistically wider in women than men. LLA and CCA were wider in women but there was no statistical difference, LLAT was bigger in women but there was no statistical difference. There was no statistical difference in measurements by age. Conclusion: &nbsp;The results of this study are important to avoid neurovascular injury and pedicle breakage because of choosing large screw while performing C1 laminar screw fixation