With the high amount of mentally seeking behaviors among students nationwide according to Healthy minds network survey (2019) and lack of related studies to students mental health in the Gulf region, a gap is created Fatima Al-Darmaki et al. (2015) this project is executed to fill the gap by utilizing data analytics techniques to generate insights that can be used to support future decisions. Students who suffer from mental disorders are most likely to disengage from university which can be translated into a dropout. Many organizations are conducting periodically to capture as many insights to tackle those behaviors. This project is aiming to tackle this problem and gather insights from a web-based survey that is answered anonymously by the target group of students. Answers will be analyzed and preprocessed by several statistical techniques, transformed and finally modeled to generate insights. Gathered insights can lead to new hypotheses to be formulated. In addition, unsupervised learning algorithms have been introduced such as Apriori and FP-growth to generate the frequent itemsets which use association rules mining to associate the different mental health factors that are related to this research. such as the association between participant sex related to mental health concern, and related sexual orientation that has more likely to visit mental health consultant services, and the relation between sexual orientation to mental disorder has been investigated to find no significant relationship. On the other hand combining Social network analysis approach with frequent pattern mining found interesting patterns in building relations between demographic features of who are more likely to consult a mental health specialist. Results and further research can be used to support decisions of future mental related behaviors and support future decisions for related problems.
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research (Dubai)
Hussein Mohsen, Abdullah, "Exploring Students Mental Behaviors Using Unsupervised Learning Algorithms and Graph Theory" (2020). Thesis. Rochester Institute of Technology. Accessed from