Safuan, Nurfadzlina and Anuar Kamal, Adriana and Hassan, Raini (2024) A data-driven approach to unveiling mental health realities among undergraduate students at the International Islamic University Malaysia (IIUM) using machine learning: a case study. In: Advancement in ICT: Exploring Innovative Solutions (AdICT). KICT Publishing, pp. 80-88.
|
PDF
- Published Version
Download (9MB) | Preview |
Abstract
This study investigated the mental health of Generation Z undergraduate students at the International Islamic University of Malaysia (IIUM), focusing on the impact of academic pressures and societal expectations. Generation Z, defined as those born between 1997 and 2012, represents a unique cohort navigating the challenges of modern education and societal norms. Data was collected from IIUM students via a Google survey for the analysis. The study compared Random Forest, Support Vector Machine, and Feed Forward Deep Learning models for predicting mental health outcomes. Random Forest achieved the highest accuracy at 0.71. Key factors influencing mental health were daily meal intake, extracurricular activities, social support, and financial stability. The results highlight the importance of effective data balancing techniques, like SMOTE, in improving model performance. These findings provide valuable insights into the mental health challenges faced by Generation Z students and emphasize the need for targeted interventions to support their well-being.
| Item Type: | Book Chapter |
|---|---|
| Uncontrolled Keywords: | mental health, generation Z, undergraduate, machine learning, SMOTE |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science |
| Depositing User: | Dr. Raini Hassan |
| Date Deposited: | 06 Jan 2025 16:29 |
| Last Modified: | 06 Jan 2025 16:35 |
| URI: | http://irep.iium.edu.my/id/eprint/117276 |
Actions (login required)
 |
View Item |
Download Statistics
Download Statistics