Methods: A survey using self-administered questionnaires was used to collect data from a sample of 856 staff nurses working in eight public hospitals in Malaysia. A shortened nine-item version of the Utrecht Work Engagement Scale(UWES-9) was used to measure work engagement. The UWES-9 comprises three dimensions, which was measured with three items each: vigor, dedication, and absorption. Job characteristics (job autonomy, job feedback, skill variety, task identity, task significance) were measured with the corresponding subscales of the Job Diagnostic Survey. Each subscale consisted of three items. Hypotheses were tested using hierarchical regression analysis.

Results: Findings indicated that all the five demographic variables (age, marital status, education, organizational tenure, job tenure) were unrelated to work engagement. The results further revealed that job autonomy (β = 0.19, P  0.05), however, did not affect work engagement.

Methods: Data from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients' adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients' adherence levels and variables were generated using SOM.

Result: Machine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern.