TITLE:
Hierarchical Method for Classifying Latent Traumatic States (CAH-ET)
AUTHORS:
Kpinna Tiekoura Coulibaly, Abdoul Maïga, Kamagaté Beman Hamidja, Diaby Moustapha
KEYWORDS:
Social Resilience, Classification, Machine Learning
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.14 No.12,
December
11,
2024
ABSTRACT: This article presents a hybrid method of automatic classification of latent traumatic states adapted to the analysis of social resilience processes. Our approach combines the Hierarchical Ascending Classification (CAH) technique with decision tree. It is primarily aimed at improving the identification and categorization of traumatic states by integrating the strengths of both methods. CAH is used to cluster data, allowing the detection of underlying patterns in traumatic states. Then, decision trees are applied to classify these clusters, providing a clear and accessible interpretation of the results. This combination not only provides a better understanding of the data structure, but also provides accurate and actionable classifications. This work highlights the importance of this hybrid method in the field of social resilience processes, particularly in psychology and psychiatry, where early detection and classification of trauma can have a significant impact on the patient’s follow-up. Experimental results show an improvement in the classification accuracy of our approach compared to a classification method for the same domain using a hybridization between the partitioning technique and genetic algorithms. This opens promising prospects for the application of this approach in clinical settings of social resilience.