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

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 multifaceted 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.