Cardiovascular diseases (CVDs) remain the leading cause of death globally, underscoring the urgent need for data-driven early diagnostic tools. This study proposes a multilayer artificial neural network (ANN) model for heart disease prediction, developed using a real-world clinical dataset comprising 13,981 patient records. Implemented on the Orange data mining platform, the ANN was trained using backpropagation and validated through 10-fold cross-validation. Dimensionality reduction via principal component analysis (PCA) enhanced computational efficiency, while Shapley additive explanations (SHAP) were used to interpret model outputs. Despite achieving 83.4% accuracy and high specificity, the model exhibited poor sensitivity to disease cases, identifying only 76 of 2233 positive samples, with a Matthews correlation coefficient (MCC) of 0.058. Comparative benchmarks showed that random forest and support vector machines significantly outperformed the ANN in terms of discrimination (AUC up to 91.6%). SHAP analysis revealed serum creatinine, diabetes, and hemoglobin levels to be the dominant predictors. To address the current study’s limitations, future work will explore LIME, Grad-CAM, and ensemble techniques like XGBoost to improve interpretability and balance. This research emphasizes the importance of explainability, data representativeness, and robust evaluation in the development of clinically reliable AI tools for heart disease detection.

With agile changes in many sectors, technology comes to the focus of our lives and helps us to take more solid steps. Concepts such as e-commerce, e-health, and data mining have come to the fore. The effects of technological advances, which have begun to integrate into health services, such as increasing work efficiency, service quality, and creating a safe service environment have been determined. In this review study, various digitalization studies carried out in the field of health between 2012–2022 were examined and summarized.

With the rapid development of technology in recent years, it is observed that there are agile changes in many sectors. With these changes, technology comes to the focus of our lives and helps to take more solid steps by facilitating processes everywhere. With the evolution of the sectors in this direction, concepts such as e-commerce, e-health and data mining have come to the fore, and many studies have been put forward within the framework of these terms. It has been observed that the digital transformation that has begun to take place in the field of healthcare has led to significant changes in this field. The effects of technological advances, which have begun to integrate into health services, such as increasing work efficiency, increasing service quality and creating a safe service environment have been determined. In this review study, various digitalization studies carried out in the field of health between 2012-2022 were examined and summarized, also, the prominent concepts in the studies were classified. In addition, it is aimed to determine the popular methods that researchers include in their studies and to examine the tools that support the application within the scope of the maturity assessment models. At this point, the study is divided into two main headings: (1) Digitalization in Health, (2) Digital Maturity Assessment Models in Health Systems. As a result of the study, it was aimed to contribute to the existing literature by observing the deficiencies in the literature.

Successful digital transformation does not only depend on the successful implementation of information systems but also depends on how effectively those systems are being utilized by people in the organization

Consumer identification with brands has received extensive attention in the fields of consumer behavior and psychology. Such consumer-brand identification is conceptualized through the term “brand community”. A brand community is formed when a group of consumers express their interest towards and communicate their identities through a specific brand. The current literature shows that existing research on brand communities appears to be dominantly oriented towards tangible products. Accordingly, this research aims to explore the impact of social media marketing on brand community membership related to an intangible aspect of the self. Contextually, the research seeks to examine the impact of social media marketing of higher education institutions in Kuwait on students’ relevant associations. Students are at the core of higher education institutions due to identifying as both consumers and creators of value. A mixed method approach to data collection and analysis will be employed in this research. The findings of this research are expected to provide important implications for theory and practice.

As industrial activities increasingly impact the environment, the need for resource conservation and long-term energy security becomes paramount, positioning sustainable electricity production as a critical focus area. In this regard, the application of Machine Learning, a branch of Artificial Intelligence, has revealed considerable promise in optimizing various dimensions of energy generation, especially within renewable sectors such as wind energy. This research presents a case study that showcases the implementation of Machine Learning techniques in forecasting the failures and establishing predictive maintenance plans for wind energy systems. In this study, we employed Logistic Regression, Decision Tree, Bagging, Random Forest, Gradient Boosting, Adaboost, and XGBoost to analyze more than 40,000 rows of data from 40 different system parts. At the end of the analysis, Tuned XGBoost model has turned out to be the best model having the highest Recall value that correctly predicts the Failures. Through the application of diverse methodologies, we demonstrate the effectiveness of these techniques in anticipating potential failures and prioritizing maintenance or replacement of parts, which in turn facilitates substantial resource savings in terms of labor, costs, and time, thereby advancing sustainability and strategic planning.