Heart attacks continue to be one of the primary causes of death globally, highlighting the critical need for advanced predictive models to improve early diagnosis and timely intervention. This study presents a comprehensive machine learning (ML) approach to heart attack prediction, integrating multiple datasets from diverse sources to construct a robust and accurate predictive model. The research employs a stacking ensemble model, which combines the strengths of individual ML algorithms to improve overall performance. Extensive data preprocessing steps were carefully undertaken to preserve the dataset's integrity and maintain its quality. The results demonstrate a superior accuracy of 97.48%, significantly outperforming state-of-the-art approaches. The high level of accuracy indicates the model’s potential effectiveness in the clinical setting for early detection of heart attack and prevention. However, the proposed model is influenced by the quality and diversity of the integrated datasets, which could affect its generalizability across broader populations. Challenges encountered during the model's development include optimizing hyperparameters for multiple classifiers, ensuring data preprocessing consistency, and balancing computational efficiency with model interpretability. The results underscore the pivotal contribution of advanced ML approaches in revolutionizing the management of cardiovascular attack. By addressing the complexities and variabilities inherent in heart attack prediction, the work provides a pathway towards more effective and personalized cardiovascular disease management strategies, demonstrating the transformative potential of ML in healthcare.

The rise of online education has changed the way students usually learn by making educational materials easier to get to and creating a global learning community. While online education offers numerous benefits, it is also crucial to acknowledge its certain drawbacks, such as the potential reduction in interaction between students and teachers, which might increase signs of isolation among students and impede opportunities for collaborative learning. Therefore, Student Evaluations of Teaching (SET) play a critical role in identifying areas for improvement from the students' standpoint, thereby promoting constructive communication between students and teachers. This research conducts a comparison among the traditional Educational Data Mining (EDM) techniques to find out the best-performing classifier for analyzing student evaluations of teaching online. It is accomplished by first extracting the dataset from the student evaluations of teaching at X-University and then applying six different classifiers to the dataset that were extracted. The results demonstrated that Logistic Regression, Naive Bayes, and K-Nearest Neighbors (KNN) exhibited a notably high level of accuracy compared to other classification techniques. The findings of this research will provide guidance for future researchers in applying a wider range of classification techniques to extensive datasets and in implementing the necessary adjustments to achieve superior results.