Abstract
The mental health issues among students are prevailing due to predictions of low mood, anxiety, depression, and academic difficulties. Late diagnosis of these mental health issues takes prolonged measures to minimise its impact. Nowadays, machine learning is used to assess mental health by detecting signals that provide information about mental health through brain scans and questionnaires. Early detection of mental health issues in students possible to control them effectively. Colleges and universities are trying to improve student care by assessing their mental health and reducing mental health problems through psychological therapies. Machine learning techniques, including Multiclass Classifier, LAD Tree, and Multilayer Perception, are valuable techniques that help to achieve effective results. The accuracy of the results is compared in the study indicating a prediction of 81.75%. The machine learning activities valuable to the undertaken study, which helps the research set perspective.
Publication Date
2022
Document Type
Master's Project
Student Type
Graduate
Degree Name
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research (Dubai)
Advisor
Sanjay Modak
Advisor/Committee Member
Ioannis Karamitsos
Recommended Citation
Sadiq, Maryam, "Predicting Students' Mental Disorders Using Machine learning" (2022). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/11413
Campus
RIT Dubai
