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 safety of online transactions is paramount in the modern world, mainly since technology develops at a dizzying rate. This study aims to shed light on the numerous threats that users of online transaction systems face. The study used a mixed-methods research strategy to investigate the experiences and perspectives of 400 individuals from various backgrounds. Worryingly, the results show a significant knowledge gap on the many types of cyber hazards. The research reveals a troubling lack of awareness about various cyber risks, including fraud, phishing, and identity theft. It highlights the user’s common functional difficulties. The study proposes a novel framework named COTSEF: A Comprehensive Framework for Enhancing Security in Online Transactions to enhance online transaction security alongside these findings. This comprehensive framework aims to provide a safer and more dependable environment for online commerce by mitigating the identified risks and challenges. The demographic breakdown of the users is also investigated, with the results indicating the increased vulnerability of some age groups and professions to various hazards. It also highlights the need for educational activities to address the significant need for more awareness about data protection rules. The study is a critical resource for policymakers, corporations, and educational institutions, offering actionable insights for developing more secure and user-friendly online transaction systems.