Search Results for Suzan S. Haydar
Abstract
The integration of smart technologies into higher education is rapidly reshaping traditional teaching and learning environments. This study investigates differences in academic performance between students utilizing AI technologies and those who do not, spanning three educational levels: undergraduate, graduate, and postgraduate. Using a simulated dataset of 200 participants, the analysis employed independent sample t-tests, chi-square tests, regression models, correlation analyses, and visualizations such as scatter plots. The findings consistently demonstrate that students who adopt AI significantly outperform their peers, with notable statistical improvements across all groups. Additionally, factors like educational level and age were found to influence outcomes, with younger students and AI users showing greater consistency and higher achievement. Beyond presenting numerical data, the research contextualizes these results within modern digital learning theories, addresses challenges in technological integration, and offers practical recommendations for effectively incorporating AI tools into higher education frameworks.
Abstract
Breast cancer is one of the most prevalent and life-threatening diseases worldwide, making early and accurate diagnosis essential for improving patient outcomes. This study aims to evaluate the performance of two machine learning models, Extreme Gradient Boosting (XGBoost) and Gaussian Naive Bayes, for breast cancer classification using the Breast Cancer Wisconsin Diagnostic dataset. A comparative analysis was conducted based on several performance metrics, including Accuracy, Precision, Recall, F1-score, Specificity, Receiver Operating Characteristic (ROC) curve, Area Under the Curve (AUC), Balanced Accuracy, and Confusion Matrix. In addition, feature importance analysis was performed to identify the most influential variables contributing to the classification process. The experimental results demonstrated that both models achieved high classification performance. However, the Gaussian Naive Bayes model showed slightly superior performance, achieving an Accuracy of 96.5% and an AUC of 0.997, compared with 96.0% and 0.993, respectively, for the XGBoost model. Furthermore, the analysis revealed that features related to tumor morphology, particularly concave points mean, perimeter worst, and radius worst, were among the most influential predictors of breast cancer classification. The findings indicate that both machine learning models provide reliable and effective tools for supporting early breast cancer diagnosis, with Gaussian Naive Bayes demonstrating a slight advantage for the dataset used in this study. These results highlight the potential of machine learning techniques to enhance diagnostic accuracy and support clinical decision-making.