Development of Early Stage Diabetes Prediction Model Based on Stacking Approach

Diabetes is a disease that may pose direct or indirect risks in terms of human health. Early diagnosis can minimize the potential harm of this disease to the body and reduce the probability of death. For this reason, laboratory tests are performed on diabetic patients. The analysis of these tests enables the diagnosis of diabetes. The aim of this study is so quickly diagnose diabetes by using data obtained from patients with machine learning methods. In order to diagnose the disease, k-nearest neighbor (k-NN), logistic regression (LR), random forest (RF) models and the stacking meta model which is created by combining these three models were used. The dataset used in the research includes test samples taken from 520 people. The dataset has 17 features, including 16 input features and 1 output feature. As a result of the classification through this dataset, different classification results were obtained from the models. The classification success of the models LR, k-NN, RF and stacking were found to be 91.3%, 91.7%, 97.9% and 99.6%, respectively. F-score, precision and recall performance metrics were utilized for a detailed analysis of the models\u27 classification results. The obtained results revealed that the stacking model has a sufficient level to be used as a decision support system in the early diagnosis of diabetes

FEATURE EXTRACTION AND RECOGNITION ON TRAFFIC SIGN IMAGES

FEATURE EXTRACTION AND RECOGNITION ON TRAFFIC SIGN IMAGESAbstractIt is vital that the traffic signs used to ensure the order of the traffic are perceived by the drivers. Traffic signs have international standards that allow the driver to learn about the road and the environment while driving. Traffic sign recognition systems have recently started to be used in vehicles in order to improve traffic safety. Machine learning methods are used in the field of image recognition. Deep learning methods increase the classification success by extracting the hidden and interesting features in the image. Images contain many features and this situation can affect success in classification problems. It can also reveal the need for high-capacity hardware. In order to solve these problems, convolutional neural networks can be used to extract meaningful features from the image. In this study, we created a dataset containing 1500 images of 14 different traffic signs that are frequently used on Turkey highways. The features of the images in this dataset were extracted using convolutional neural networks from deep learning architectures. The 1000 features obtained were classified using the Random Forest method from machine learning algorithms. 93.7% success was achieved as a result of this classification process.Keywords: Classification, Convolution neural network, Feature extraction, Random forest, Traffic signsTRAFİK İŞARETİ GÖRÜNTÜLERİNDE ÖZELLİK ÇIKARMA VE TANIMAÖzetTrafiğin düzenini sağlamak amacıyla kullanılan trafik levhalarını sürücülerin algılaması hayati önem taşımaktadır. Sürüş esnasında sürücünün yol ve çevre hakkında bilgi edinebilmesini sağlayan trafik levhaları uluslararası standartlara sahiptir. Trafik levhası tanıma sistemleri son zamanlarda trafik güvenliğini arttırmak amacıyla araçlarda kullanılmaya başlamıştır. Makine öğrenmesi yöntemleri görüntü tanıma alanında kullanılmaktadır.  Derin öğrenme yöntemleri, görüntüde yer alan gizli ve ilginç özellikleri çıkarak sınıflandırma başarısını arttırmaktadır. Görüntüler çok sayıda özellik içermektedir ve bu durum sınıflandırma problemlerinde başarıyı etkileyebilmektedir. Ayrıca yüksek kapasiteli donanım gereksinimini de ortaya çıkarabilmektedir. Bu sorunların çözülebilmesi için görüntüden anlamlı özelliklerin çıkarılmasında konvolüsyonel sinir ağları kullanılabilmektedir. Bu çalışmada Türkiye’deki karayollarında sıklıkla kullanılan 14 farklı trafik levhasına ait 1500 görüntü içeren bir veriseti tarafımızca oluşturulmuştur. Bu veriseti kullanılarak derin öğrenme mimarilerinden konvolüsyonel sinir ağları kullanılarak görüntülerin özellikleri çıkarılmıştır. Elde edilen 1000 özellik makine öğrenmesi algoritmalarından Random Forest yöntemi kullanılarak sınıflandırılmıştır. Bu sınıflandırma işlemi sonucunda %93.7 başarı elde edilmiştir.Anahtar Kelimeler: Konvolüsyonel sinir ağları, Özellik çıkarma, Random forest, Sınıflandırma, Trafik işaretleri

Echocardiographic evaluation of diastolic functions in patients with polycystic ovary syndrome: A comperative study of diastolic functions in sub-phenotypes of polycystic ovary syndrome

Background: Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder among reproductive-aged women. It is known to be associated with cardiovascular diseases. The aim of this study was to determine and compare the echocardiographic data of patients according to the phenotypes of PCOS. Methods: This study included 113 patients with PCOS and 52 controls. Patients were classified into four potential PCOS phenotypes. Laboratory analyses and echocardiographic measurements were performed. Left ventricular mass was calculated by using Devereux formula and was indexed to body surface area. Results: Phenotype-1 PCOS patients had significantly higher homeostasis model assessment — insu­lin resistance (HOMA-IR) (p = 0.023), free testosterone (p < 0.001), LDL cholesterol levels (p < 0.001) and free androgen index (p < 0.001) compared with the control group. There were significant differences between groups regarding the septal thickness, posterior wall thickness, Left ventricular ejection frac­tion, E/A ratio and left ventricular mass index (for all, p < 0.05). PCOS patients with phenotype 1 and 2 had significantly higher left ventricular mass index than the control group (p < 0.001). In univariate and multivariate analyses, PCOS phenotype, modified Ferriman-Gallwey Score and estradiol were found as variables, which independently could affect the left ventricular mass index. Conclusions: This study showed that women in their twenties who specifically fulfilled criteria for PCOS phenotype-1 according to the Rotterdam criteria, had higher left ventricular mass index and decreased E/A ratio, which might be suggestive of early stage diastolic dysfunction. (Cariol J 2017; 24, 4: 364–373

Determining the Extinguishing Status of Fuel Flames With Sound Wave by Machine Learning Methods

Fire is a natural disaster that can be caused by many different reasons. Recently, more environmentally friendly and innovative extinguishing methods have started to be tested, some of which are also used. For this purpose, a sound wave fire-extinguishing system was created and firefighting tests were performed. With the data obtained, as a result of 17,442 tests, a data set was created. In this study, five different machine learning methods were used by using the data set created. These are artificial neural network, k-nearest neighbor, random forest, stacking and deep neural network methods. Stacking method is an ensemble method created by using artificial neural network, k-nearest neighbor, random forest models together. Classification of extinction and non-extinction states of the flame was made with the models created with these methods. The accuracy of models in classification should be analyzed in detail in order to be used as a decision support system in the sound wave fire-extinguishing system. Hence, the classification processes were carried out through the 10-fold cross-validation method. As a result of these tests, the performance analysis of the models was carried out, and the results showed that the highest classification accuracy belongs to the stacking model with 97.06%. The classification accuracy was determined 96.58% in random forest method, 96.03% in artificial neural network model, 94.88% in deep neural network model and 92.62% in k-NN model. The performance of the methods was compared by analyzing the performance metrics of machine learning methods. Thanks to the decision support system to be obtained based on the results of the analyzes, the sound wave fire-extinguishing system can be used efficiently

The Colonic Tissue Levels of Tlr2, Tlr4 And Nitric Oxide in Patients with Irritable Bowel Syndrome

Objective Irritable bowel syndrome (IBS) is a highly prevalent and debilitating functional disorder. The toll-like receptors (TLRs) are a family of pathogen-recognition receptors in the innate immune system. In the present study we aimed to investigate the TLR2, TLR4 and nitric oxide (NO) levels in patients with IBS. Methods Fifty-one IBS patients and 15 healthy controls were included in the present study. Colonic tissue levels of TLR2, TLR4 and NO were detected using an enzyme-linked immunosorbent assays (ELISA) and through biochemical methods. Results The colonic tissue levels of TLR4 and NO were significantly higher in IBS patients than in healthy controls. A subgroup analysis, which was based on the presence of diarrhea and constipation, showed that TLR2 levels were significantly higher among individuals with diarrhea-predominant IBS than among constipation-predominant IBS patients and healthy controls. The TLR4 levels were significantly higher in the diarrhea-predominant IBS patients and constipation-predominant IBS patients than in comparison healthy controls. The colonic tissue levels of NO were higher in the constipation-predominant IBS patients than in the diarrhea-predominant IBS patients and healthy controls. Conclusion In the present study we found that the colonic tissue levels of TLR and NO were elevated in IBS patients. Our results support the presence of a degree of immune dysregulation and oxidative stress in patients with IBS.Wo

A case of reversible cardiomyopathy associated with acute toluene exposure

Inhalation of toluene-based products is popular among young adults. It has been shown to have a variety of adverse effects on several organs and systems. Although the heart seems to be a sensitive target organ to toluene, cardiotoxicity has often been ignored, especially in cases of acute toluene abuse, with relatively low concentrations. Thereby, routine cardiac examination and echocardiography for cardiotoxicity should be performed in cases of acute toluene exposure, even though there is no cardiovascular sign or symptoms. Keywords: Toluene, Acute cardiac toxicity, Cardiomyopath

The course of post-stroke bladder problems and their relation with functional and mental status and quality of life: A six-month, prospective, multicenter study

WOS: 000511167800006PubMed: 31893270Objectives: the aim of this study was to evaluate the frequency and course of post-stroke lower urinary tract dysfunction (LUTD) from early term up to a period of six months and to investigate the relation of LUTD with functional and mental status and quality of life (QoL) in stroke patients. Patients and methods: This prospective study included a total of 70 stroke patients (44 males, 26 females; mean age 62.7 +/- 7.0 years; range, 46 to 79 years) from five different centers across Turkey between June 2015 and January 2017. the patients were questioned using the Danish Prostatic Symptom Score (DAN-PSS) to evaluate LUTD and evaluated using the Modified Barthel Index (MBI), Incontinence QoL Questionnaire (I-QOL), and Mini-Mental State Examination (MMSE) at one, three, and six months. Results: At least one symptom of LUTD was observed in 64 (91.4%), 58 (82.9%), and 56 (80%) of the patients according to the DAN-PSS at one, three, and six months, respectively. A statistically significant improvement was found in the DAN-PSS, MBI, MMSE, I-QOL total scores, avoidance and psychosocial subgroup scores at six months compared to the first month scores (p<0.05). There was a significant negative correlation between the DAN-PSS symptom score at one month and the MBI, MMSE, and QoL scores at six months. the DAN-PSS bother and total scores were found to be significantly and negatively correlated only with the subscales of the QoL questionnaire. Conclusion: Based on our study results, LUTD was very common and the prevalence of LUTD findings decreased constantly during six-month follow-up, showing an association with a poor cognitive and functional status and QoL in stroke patients with LUTD