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Purpose. Diagnose keratoconus by establishing an effective logistic regression model from the data obtained with a Scheimpflug-Placido cornea topographer. Methods. Topographical parameters of 125 eyes of 70 patients diagnosed with keratoconus by clinical or topographical findings were compared with 120 eyes of 63 patients who were defined as keratorefractive surgery candidates. The receiver operating character (ROC) curve analysis was performed to determine the diagnostic ability of the topographic parameters. The data set of parameters with an AUROC (area under the ROC curve) value greater than 0.9 was analyzed with logistic regression analysis (LRA) to determine the most predictive model that could diagnose keratoconus. A logit formula of the model was built, and the logit values of every eye in the study were calculated according to this formula. Then, an ROC analysis of the logit values was done. Results. Baiocchi Calossi Versaci front index (BCVf) had the highest AUROC value (0.976) in the study. The LRA model, which had the highest prediction ability, had 97.5% accuracy, 96.8% sensitivity, and 99.2% specificity. The most significant parameters were found to be BCVf (p=0.001), BCVb (Baiocchi Calossi Versaci back) (p=0.002), posterior rf (apical radius of the flattest meridian of the aspherotoric surface in 4.5 mm diameter of the cornea) (p=0.005), central corneal thickness (p=0.072), and minimum corneal thickness (p=0.494). Conclusions. The LRA model can distinguish keratoconus corneas from normal ones with high accuracy without the need for complex computer algorithms.

Diabetes care at school has recently appeared on the agenda of international diabetes organizations, the basic principles of which have been newly determined. The aim of this review was to summarize the activities and output of the Diabetes at School Program - a program that has been delivered in Turkey for the last 10 years - and to focus on different aspects of Diabetes Care at School through a national model. Recently, a detailed set of national regulations, including the basic principles proposed by the International Society for Pediatric and Adolescent Diabetes and the experience in Turkey, was prepared and has come into force. The future agenda includes giving priority to socio-economically disadvantaged regions, provision of an Individual Treatment Plan at School for each child with diabetes and ensuring that each school has an action plan for the care of children with diabetes. We believe that if all countries have programs and structured national regulations similar to the Diabetes at School Program, this will enable significant progress in the level of care delivered to children with diabetes.

In parallel with the increase in internet usage, people from different parts of the world can easily convey their thoughts and feelings on social issues through social media. Millions of messages are written and read every day on various topics on a global scale through Twitter, which has an important place in these social media. While it is important to understand consumer behaviors in order to increase the competitiveness of firms, big data sources such as Twitter have multi-faceted the methods of analyzing behaviors. At the same time, developed countries allocate significant resources to data mining projects in order to have power. The use of Twitter and data mining as an alternative data source to identify trends in furniture choice has been proposed. The popular tweets with furniture using the Rapidminer and natural language processing software were gathered for ten months between May 2018 and February 2019, and natural language processing software enabled us to determine the mood of the tweets (positive and negative). Morphological analysis of the keywords in positive and negative tweets was then performed. Finally, meaningful information was obtained by utilizing the decision tree and association algorithms used in data mining. According to the decision tree algorithm, the most dominant words in the formation of positive or negative emotions were the challenge, campaign, discover and idea. As a result of the syntax of association, the most positive emotions were made with the order of words that awaken the emotions, and the opportunity was found as wood. In the same algorithm, the words that awaken the most negative emotions were listed as gloom, seedy, uncomfortable and fabric.