Fereshteh Parsapour; Javid Peymani; Mohammad Khanbabaei
Abstract
Introduction: Diagnosing anxiety in the early stages by psychiatrists is one of the important steps in preventing and controlling these types of disorders. This study endeavors to present a method to diagnose the severity of anxiety using fuzzy C-means clustering (FCM) algorithm. Moreover, the influence ...
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Introduction: Diagnosing anxiety in the early stages by psychiatrists is one of the important steps in preventing and controlling these types of disorders. This study endeavors to present a method to diagnose the severity of anxiety using fuzzy C-means clustering (FCM) algorithm. Moreover, the influence of each feature on measuring anxiety and clustering of clients is determined.Methods: This was a quantitative and descriptive study with a dataset including 300 clients related to three psychiatric clinics in Tehran, Iran provided based on the Beck Anxiety Inventory (BAI). Then, the FCM algorithm was utilized to segment the clients and determine the severity of their anxiety in each cluster. Additionally, this algorithm was employed for each feature separately.Results: The psychiatric clinics' clients were divided into four clusters with the labels including no, minimal, moderate, and severe anxiety. Using the FCM algorithm, the anxiety of the clients was diagnosed with 90.66% accuracy. Moreover, as a result of implementing the algorithm on each feature, the influence of the features on measuring anxiety and clustering of clients was determined.Conclusion: The FCM algorithm diagnosed the anxiety of clients with a high accuracy. Segmenting patients by the clustering approach and based on the important features can be a dependable instrument for psychiatrists to make a decision in diagnosing the severity of anxiety in the early stages.
