Implementation of K-Nearest Neighbor in Case-Based Reasoning for Mental Health Diagnosis Systems

Abstract

Purpose: Assessing a model that employs the K-Nearest Neighbor (KNN) technique within Case-Based Reasoning (CBR) for diagnosing mental health disorders, concentrating on conditions such as anxiety, depression, stress, and normalcy, while enhancing its efficacy through the utilization of historical case data for more accurate and tailored diagnostic suggestions.

Methods: This study implements the KNN method in CBR to create a mental health diagnosis system that can provide accurate results without the need for complex models or intensive training. This method effectively addresses various patient needs by utilizing previous case data to provide a personalized and case-based diagnosis. This system is designed to tackle mental health issues like anxiety, depression, and academic stress, utilizing a case study of students from ITBK Bukit Pengharapan.

Result: This study developed a KNN-based model for mental health diagnosis, achieving 84.62% accuracy on test data. Data processing techniques like text mining, oversampling, and cosine similarity improved performance. With an optimal K value of 2, the model achieved 88% precision, 85% recall, and an F1-score of 84%. The anxiety label performed perfectly, with 100% precision, recall, and F1-score.

Novelty: This study adds innovation by integrating the rarely used CBR and KNN algorithms for mental health diagnosis systems. Innovative techniques like text mining, oversampling to get around data integration, and cosine similarity computations, which greatly enhance model performance, assist this strategy. Because this method improves accuracy and expedites the diagnosis process, both of which support clinical decision-making, it may be able to help mental health professionals.