Cover Image

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

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 n...

Full description

Saved in:
Bibliographic Details
Main Authors: Safuan, Nurfadzlina, Anuar Kamal, Adriana, Hassan, Raini
Format: Book Chapter
Language:English
Published: KICT Publishing 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://irep.iium.edu.my/117276/1/AdICT%20Series%202-2024.pdf
http://irep.iium.edu.my/117276/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.iium.irep.117276
record_format dspace
spelling my.iium.irep.1172762025-01-06T08:35:47Z http://irep.iium.edu.my/117276/ 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 Safuan, Nurfadzlina Anuar Kamal, Adriana Hassan, Raini QA75 Electronic computers. Computer science 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. KICT Publishing 2024-12-19 Book Chapter NonPeerReviewed application/pdf en http://irep.iium.edu.my/117276/1/AdICT%20Series%202-2024.pdf 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.
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Safuan, Nurfadzlina
Anuar Kamal, Adriana
Hassan, Raini
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
description 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.
format Book Chapter
author Safuan, Nurfadzlina
Anuar Kamal, Adriana
Hassan, Raini
author_facet Safuan, Nurfadzlina
Anuar Kamal, Adriana
Hassan, Raini
author_sort Safuan, Nurfadzlina
title 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
title_short 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_sort data-driven approach to unveiling mental health realities among undergraduate students at the international islamic university malaysia (iium) using machine learning: a case study
publisher KICT Publishing
publishDate 2024
url http://irep.iium.edu.my/117276/1/AdICT%20Series%202-2024.pdf
http://irep.iium.edu.my/117276/
_version_ 1821105132706725888
score 13.235362

Similar Items