Unveiling Longitudinal Patterns in International Mental Health Assessments: A Systematic Evaluation of Clustering Techniques

John Makunda, Helen Waititu and Cornelius Nyakundi
Additional contact information
John Makunda: Department of Mathematics and Actuarial Science, Catholic University of Eastern Africa
Helen Waititu: Department of Mathematics and Actuarial Science, Catholic University of Eastern Africa
Cornelius Nyakundi: Department of Mathematics and Actuarial Science, Catholic University of Eastern Africa

International Journal of Research and Innovation in Social Science, 2024, vol. 8, issue 9, 530-547

Abstract: Background: Mental health assessments across diverse populations provide valuable insights into the prevalence and patterns of mental health issues. However, the complexity and volume of longitudinal data present challenges in extracting meaningful information for effective intervention. Clustering methods have emerged as powerful tools for identifying hidden structures within such datasets, yet a comprehensive evaluation of these techniques in the context of international mental health assessments is lacking. Objectives: This study aims to systematically evaluate various clustering techniques applied to longitudinal mental health data from international assessments. The focus is on understanding how different methods capture and reveal patterns and subgroups within the data, thereby guiding targeted mental health interventions. Methods: We applied and compared three clustering techniques—K-Means Clustering, Hierarchical Clustering, and Gaussian Mixture Models (GMM)—to longitudinal mental health assessment data. We assessed the performance of these methods in identifying meaningful clusters, considering their strengths and limitations in capturing the complexity of mental health trajectories. Results: Our analysis revealed distinct clusters reflecting varying levels of mental health severity and symptom trajectories. K-Means identified broad clusters, while Hierarchical Clustering provided insights into the data’s hierarchical structure. GMM offered a probabilistic view, highlighting overlapping mental health experiences among individuals. Each method contributed uniquely to understanding the longitudinal patterns in the data. Implications: The findings underscore the importance of using a multi-faceted approach to clustering in mental health research. By revealing different dimensions of mental health trajectories, this study provides valuable insights for tailoring interventions and resource allocation. The results highlight the need for ongoing evaluation of clustering techniques to enhance their applicability in diverse international contexts.

Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijriss/ ... -issue-9/530-547.pdf (application/pdf)
https://rsisinternational.org/journals/ijriss/arti ... ustering-techniques/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bcp:journl:v:8:y:2024:i:9:p:530-547

Access Statistics for this article

International Journal of Research and Innovation in Social Science is currently edited by Dr. Nidhi Malhan

More articles in International Journal of Research and Innovation in Social Science from International Journal of Research and Innovation in Social Science (IJRISS)
Bibliographic data for series maintained by Dr. Pawan Verma ().