Using Fuzzy C-means Clustering Algorithm to Diagnose the Severity of Anxiety

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Parsapour Fereshteh; PEYMANI JAVID; KHANBABAEI MOHAMMAD

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Health Information Management Year:2020 | Volume:17 | Issue:3 (73) Page(s): 97-103

Download Full-Text

Persian Verion

View:

484

Download:

Cites:

Information Journal Paper

Title

Using Fuzzy C-means Clustering Algorithm to Diagnose the Severity of Anxiety

Author(s)

Parsapour Fereshteh | PEYMANI JAVID | KHANBABAEI MOHAMMAD | Issue Writer Certificate

Keywords

Cluster AnalysisQ1

Fuzzy LogicQ1

AnxietyQ1

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 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.

Cites

No record.

References

Cite

APA: Copy

Parsapour, Fereshteh, PEYMANI, JAVID, & KHANBABAEI, MOHAMMAD. (2020). Using Fuzzy C-means Clustering Algorithm to Diagnose the Severity of Anxiety. HEALTH INFORMATION MANAGEMENT, 17(3 (73) ), 97-103. SID. https://sid.ir/paper/402270/en

Vancouver: Copy

Parsapour Fereshteh, PEYMANI JAVID, KHANBABAEI MOHAMMAD. Using Fuzzy C-means Clustering Algorithm to Diagnose the Severity of Anxiety. HEALTH INFORMATION MANAGEMENT[Internet]. 2020;17(3 (73) ):97-103. Available from: https://sid.ir/paper/402270/en

IEEE: Copy

Fereshteh Parsapour, JAVID PEYMANI, and MOHAMMAD KHANBABAEI, “Using Fuzzy C-means Clustering Algorithm to Diagnose the Severity of Anxiety,” HEALTH INFORMATION MANAGEMENT, vol. 17, no. 3 (73) , pp. 97–103, 2020, [Online]. Available: https://sid.ir/paper/402270/en

Related Journal Papers