Mathematical modelling of doctors' perceptions in the diagnosis of depression: a novel approach
by Subhagata Chattopadhyay
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 11, No. 1, 2013

Abstract: This paper is a novel attempt to model a perception process in the diagnosis of depression. In order to do so, two Fuzzy Clustering Techniques (FCT), such as Fuzzy C-means (FCM) and Fuzzy k-Nearest Neighbour (FkNN) are applied. Both the techniques have a special parameter called as 'cluster fuzzifier' (m), which determines the degree of fuzziness between any two clusters. Hence, by varying 'm', one can manipulate the partition between the clusters. Thus, appropriate tuning of 'm' is critical to obtain the desired number of good quality clusters. The paper proposes that 'm' mathematically mimics doctors' diagnostic perceptions, which needs to be tuned appropriately for making a correct diagnosis, i.e. assigning appropriate class labels of depression. Having proposed this, the paper examines how 'm' influences the clustering task, on a sample of real-world depression cases.

Online publication date: Sat, 27-Sep-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com