All the educational organizations mainly aim at elevating the academic performance of students for improving the overall quality of education. In this direction, Educational Data Mining (EDM) is a rapidly trending research area that utilizes the essence of Data Mining (DM) concepts to help academic institutions figure out useful information on the Student Satisfaction Level (SSL) with the Online Learning process (OL) during COVID-19 lock-down. Different practices have been tried with EDM to predict students’ behaviors to reach the best educational settings. Therefore, Feature Selection (FS) is typically employed to find the most relevant subset of features with minimum cardinality. As the predictive accuracy of a satisfaction model is significantly influenced by the FS process, the effectiveness of the SSL model is elaborately studied in this paper in connection with FS techniques. In this connection, a dataset was first collected online via a questionnaire of student reviews on OL courses. Using this datatset, the performance of wrapper FS techniques in DM and classification algorithms was analyzed in terms of fitness values. Ultimately, the goodness of subsets with different cardinalities is evaluated in terms of prediction accuracy and number of selected features by measuring the quality of 11 wrapper-based FS algorithms and the $k$ -Nearest Neighbor ( $k$ -NN) and Support Vector Machine (SVM) as base-line classifiers. Based on the experiments, the optimal dimensionality of the feature subset was revealed, as well as the best method. The findings of the present study evidently support the well-known conjunction of the existence of minimum number of features and an increase in predictive accuracy. It is remarkable the relevancy of FS for high-accuracy SSL prediction, as the relevant set of features can effectively assist in deriving constructive educational strategies. Our study contributes a feature size reduction of up to 80% along with up to 100% classification accuracy on the adopted real-time dataset.

The healthcare sector faces intricate challenges that demand innovative solutions to enhance patient outcomes and streamline operations. The advent of Artificial Intelligence (AI) has unleashed groundbreaking potential in numerous healthcare domains, including diagnostics, patient care, and disease management. This study explores the incorporation of AI-driven methodologies for the advanced monitoring of diabetes and kidney diseases. It underscores the development of predictive models that utilize six Machine Learning (ML) and four deep learning (DL) models: Our comprehensive data analysis and rigorous model evaluation showcase AI’s capability to significantly enhance clinical practices, fostering a proactive healthcare environment marked by precision, personalization, and predictive care. Our results demonstrate substantial enhancements in the accuracy of disease monitoring. For diabetes prediction, the Gradient Boosting (GB) and Random Forest (RF) models achieved up to 89.61% accuracy, while the hybrid LSTM+CNN model outperformed other DL models with an accuracy of 89.7%. For kidney disease prediction, the RF model reached 97.5% accuracy, and the LSTM-CNN model demonstrated a remarkable accuracy of 98.9%. These findings underscore the transformative potential of AI in healthcare, fostering a proactive environment characterized by precision, personalization, and predictive care. Integrating AI within CURA’s virtual wards facilitates earlier disease detection and timely interventions and enables more tailored treatment plans, ultimately optimizing healthcare delivery and patient management.