Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can help people to change their lifestyles and to ensure proper medical treatment if necessary. In this research, ten machine learning (ML) classifiers from different categories, such as Bayes, functions, lazy, meta, rules, and trees, were trained for efficient heart disease risk prediction using the full set of attributes of the Cleveland heart dataset and the optimal attribute sets obtained from three attribute evaluators. The performance of the algorithms was appraised using a 10-fold cross-validation testing option. Finally, we performed tuning of the hyperparameter number of nearest neighbors, namely, ‘k’ in the instance-based (IBk) classifier. The sequential minimal optimization (SMO) achieved an accuracy of 85.148% using the full set of attributes and 86.468% was the highest accuracy value using the optimal attribute set obtained from the chi-squared attribute evaluator. Meanwhile, the meta classifier bagging with logistic regression (LR) provided the highest ROC area of 0.91 using both the full and optimal attribute sets obtained from the ReliefF attribute evaluator. Overall, the SMO classifier stood as the best prediction method compared to other techniques, and IBk achieved an 8.25% accuracy improvement by tuning the hyperparameter ‘k’ to 9 with the chi-squared attribute set.

The technological evolution has formed new challenges for organizations to safeguard their information as digital assets. Information Security Awareness (ISA) is the cognitive state where individuals comprehend information security, threats, and the capability to develop preventive strategies. Prior studies discovered that human mistakes or misbehavior is the most vulnerable link in information security due to insufficient security awareness. There were massive data breaches reported throughout the years globally. Literature shows that individuals will develop their evaluations of risks and sense of security awareness when receiving security risk information such as data breach incidents. These indications motivated us to examine the effect of an unexplored factor, that is, data breach publicity (DBR) on ISA. The purpose of this research is to discover if DBR significantly improves a model's ability to predict ISA and its magnitude in influencing ISA. A 3-stage hierarchical linear regression approach was used to build up the model with prior known influential factors to predict ISA. To the extent of our knowledge, there is no study reported to date regarding the implication of DBR on ISA. Our main findings reveal that DBR significantly explains 6.7% of ISA and achieves the highest coefficient comparing with prior known factors. Our research contributes to a novel discovery of a new factor that significantly influences ISA and its magnitude in increasing ISA. This discovery implies the need to incorporate the knowledge of data breach incidents into ISA-related educative programs or strategies to increase ISA.

Medical science-related studies have reinforced that the prevalence of coronary heart disease which is associated with the heart and blood vessels has been the most significant cause of health loss and death globally. Recently, data mining and machine learning have been used to detect diseases based on the unique characteristics of a person. However, these techniques have often posed challenges due to the complexity in understanding the objective of the datasets, the existence of too many factors to analyze as well as lack of performance accuracy. This research work is of two-fold effort: firstly, feature extraction and selection. This entails extraction of the principal components, and consequently, the Correlation-based Feature Selection (CFS) method was applied to select the finest principal components of the combined (Cleveland and Statlog) heart dataset. Secondly, by applying datasets to three single and three ensemble classifiers, the best hyperparameters that reflect the pre-eminent predictive outcomes were investigated. The experimental result reveals that hyperparameter optimization has improved the accuracy of all the models. In the comparative studies, the proposed work outperformed related works with an accuracy of 97.91%, and an AUC of 0.996 by employing six optimal principal components selected from the CFS method and optimizing parameters of the Rotation Forest ensemble classifier.