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Article Citation - WoS: 1Citation - Scopus: 1Classifying the Who European Countries by Noncommunicable Diseases and Risk Factors(Elsevier Ireland Ltd, 2025) Bulut, TevfikBackground: In the twenty-first century, noncommunicable diseases (NCDs) are a major obstacle to global development and the accomplishment of the Sustainable Development Goals set forth by the United Nations. The WHO (World Health Organization) European Region lacks comprehensive understanding of NCD risk factors, the NCDs they trigger, and the more disadvantaged countries. Objective: This study aims to classify the countries in the European Region at the country level based on NCDs and their key risk factors. Methods: The Ward method, a hierarchical clustering technique based on Manhattan and Euclidean distance measures, was used. The study's dataset comes from the WHO's publicly available NCDs and key risk factors dataset. Results: The European region's countries have been categorized into two clusters based on key NCD risk factors. The second cluster consists of countries with high income levels. On the other hand, in the European Region, countries fall into three clusters based on NCDs. Countries in the third cluster, which consists of low- and upper- middle-income countries, have lower average values in four variables compared to other countries, resulting in lower overall disease prevalence. Counclusions: The prevalence of NCDs varies among clusters, with high-income countries having lower disease prevalence, particularly in diabetes and hypertension. Addressing risk factors and improving healthcare access and infrastructure are crucial in reducing the burden of NCDs in the European region.Master Thesis Kobi'lerin Rekabet Gücünün Artırılmasında Kümelemenin Rolü ve Önemi(2011) Tiftik, Hidayet; Tan, AyhanTürkiye'deki KOBİ'lerin finansman, pazarlama, tedarik, yönetim ve teknoloji ile ilgili ciddi problemleri bulunmaktadır. Bu problemler, işletmelerin büyük ve kurumsallaşmış firmalarla rekabet etmelerini engellemektedir.Türkiye ekonomisinde çok ciddi bir yeri olan bu işletmelerin üretim üstünlüğü, pazarlama becerisi, mali gücü, siyasi ? iktisadi ortamı oluşturabilmeleri için, öncelikle serbest piyasa koşulları içerisinde olan Türkiye de, bu problemleri aşmaları gerekmektedir.Bu araştırmada KOBİ'lerin rekabet gücüne ulaşabilmelerinde Kümelemenin önemi üzerinde durulmuştur.Bu amaç doğrultusunda geniş kapsamlı literatür taraması yapılmıştır.Sonuç olarak, Türkiye'deki küçük ve orta ölçekli sanayi işletmeleri finansman, pazarlama, tedarik, teknoloji ve yönetim sorunlarını çözebilmeleri, kısaca yeterli rekabet gücünün oluşturabilmeleri için, kümelemenin en ideal yöntem olduğu önerilmiştir.Master Thesis Küçük ve orta ölçekli işletmelerde iş zekası ihtiyaç analizi(2016) Işık, Fatih; Erkan, Turan ErmanBu çalışmada, Türkiye'deki çeşitli küçük ve orta ölçekli işletmeler üzerinde yapılan ihtiyaç analizi kapsamında İş Zekası kullanımının faydaları üzerinde irdeleme yapacak şekilde bir uygulama gerçekleştirilmiştir.Article Predicting Stroke Risk Using Machine Learning: A Data-Driven Approach to Early Detection and Prevention(Wiley, 2025) Sutcu, Muhammed; Jouda, Dana; Yildiz, Baris; Katrib, Juliano; Almustafa, Khaled MohamadStroke is a major global health concern and a leading cause of disability and mortality, emphasizing the need for early risk prediction and intervention. This study leverages statistical analysis, machine learning (ML) classification, clustering, and survival modeling to identify key stroke predictors using a dataset of 5110 records. Descriptive statistics reveal that age, glucose levels, BMI, hypertension, and heart disease are the most influential risk factors. Stroke prevalence is notably higher among hypertensive (13.25%) and heart disease patients (17.03%), as well as among former (7.91%) and current smokers (5.32%). Clustering analysis using PCA and t-SNE highlights high-risk groups with elevated glucose levels and advanced age. Among ML models, XGBoost offers the best trade-off between precision and recall, while na & iuml;ve Bayes achieves the highest recall (0.404), detecting more stroke cases despite higher false positives. Feature importance analysis ranks glucose, BMI, and age as dominant predictors, with XGBoost emphasizing cardiovascular conditions. Survival analysis confirms increasing stroke risk beyond age 60, with the Kaplan-Meier and Cox models showing a 31.9% risk increase linked to hypertension. These findings underscore the importance of early screening, lifestyle intervention, and targeted care. Future research should explore data-balancing methods like SMOTE and develop real-time tools to support clinical decision-making.
