Cardiovascular diseases (CVDs) continue to be a prominent cause of global mortality, necessitating the development of effective risk prediction models to combat the rise in heart disease (HD) mortality rates. This work presents a novel dual-stage stacked machine learning (ML) based computational risk prediction model for cardiac disorders. Leveraging a dataset that includes eleven significant characteristics from 1190 patients from five distinct sources, five ML classifiers are utilized to create the initial prediction model. To ensure robustness and generalizability, the classifiers are cross-validated ten times. The model performance is optimized by employing two hyperparameter tuning approaches: RandomizedSearchCV and GridSearchCV. These methods aim to find the optimal estimator values. The highest-performing models, specifically Random Forest, Extreme Gradient Boost, and Decision Tree undergo additional refinement using a stacking ensemble technique. The stacking model, which leverages the capabilities of the three models, attains a remarkable accuracy rate of 96%, a recall value of 0.98, and a ROC-AUC score of 0.96. Notably, the rate of false-negative results is below 1%, demonstrating a high level of accuracy and a non-overfitted model. To evaluate the model’s stability and repeatability, a comparable dataset consisting of 1000 occurrences is employed. The model consistently achieves an accuracy of 96.88% under identical experimental settings. This highlights the strength and dependability of the suggested computer model for predicting the risk of cardiac illnesses. The outcomes indicate that employing this two-step stacking ML method shows potential for prompt and precise diagnosis, hence aiding the worldwide endeavor to decrease fatalities caused by heart disease.

Augmented reality can provide an interactive learning environment. This leads to an exploration of new possibilities in the educational domain. In this work, a systematic literature review of AR applications developed for preschool and primary education is presented. To conduct this review, the Google Scholar database was used. The articles were screened using a set of five inclusion-exclusion criteria for suitability. In total, seventy-one articles published between 2012 to 2021 were selected. Of those, forty-five are conference papers, one is a book chapter, and the remaining twenty-five are journals. These works were analyzed by nine research questions proposed by us. We identified enjoyment, engagement, and learning gains as the key achievements of AR-based applications. Besides this, improvement in motivation, learning interest, participation, understanding, and self-learning are also found important in our review. Although, there are challenges—usability issues, technical problems, acceptance, cognitive overload, and health issues; however, due to their numerous benefits, AR-based applications may be another mode of teaching-learning method in the near future.

Technology is the use of scientific knowledge to accomplish practical objectives. It has the power to completely transform the way we learn and communicate. However, education is also necessary for people to have a more positive outlook on life. It fosters the growth of decision-making and problem-solving abilities. A healthy learning environment can be created with the assistance of technology. Technology can make learning more accessible, understandable, and enjoyable.