METHODS: Self-administered questionnaires were distributed to 233 undergraduate dental students involved with clinical teaching. This modified and validated questionnaire focusing on students' learning environment was used in order to gain relevant information related to dental clinical teaching. Six domains with different criteria applicable to clinical teaching in dentistry were selected consisting of modelling (four criteria), coaching (four criteria), scaffolding (four criteria), articulation (four criteria), reflection (two criteria) and general learning environment (six criteria). Data analyses were performed using IBM SPSS Statistics 20.

RESULTS: Majority of the students expressed positive perceptions on their clinical learning experience towards the clinical teachers in the Faculty of Dentistry, University of Malaya, in all criteria of the domains. Few negative feedbacks concerning the general learning environment were reported.

METHODS: This study used available under-five nutritional secondary data from the Demographic and Health Surveys performed in sub-Saharan African countries. The research used bagging, boosting, and voting algorithms, such as random forest, decision tree, eXtreme Gradient Boosting, and k-nearest neighbors machine learning methods, to generate the MVBHE model.

RESULTS: We evaluated the model performances in contrast to each other using different measures, including accuracy, precision, recall, and the F1 score. The results of the experiment showed that the MVBHE model (96%) was better at predicting malnutrition than the random forest (81%), decision tree (60%), eXtreme Gradient Boosting (79%), and k-nearest neighbors (74%).