Medical data classification using similarity measure of fuzzy soft set based distance measure

Lashari, Saima Anwar and Ibrahim, Rosziati and Senan, Norhalina (2017) Medical data classification using similarity measure of fuzzy soft set based distance measure. Journal of Telecommunication, Electronic and Computer Engineering, 9 (2-9). pp. 95-99. ISSN 2289-8131

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Abstract

Medical data classification plays a crucial role in many medical imaging applications by automating or facilitating the delineation of medical images. A considerable amount of literature has been published on medical images classification based on data mining techniques to develop intelligent medical decision support systems to help the physicians. This paper assesses the performance of a new classification algorithm using similarity measure fuzzy soft set based distance based for numerical medical datasets. The proposed modelling comprises of five phases explicitly: data acquisition, data pre-processing, data partitioning, classification using FussCyier and performance evaluation. The proposed classifier FussCyier is evaluated on five performance matrices’: accuracy, precision, recall, F-Micro and computational time. Experimental results indicate that the proposed classifier performed comparatively better with existing fuzzy soft classifiers.

Item Type:	Article
Uncontrolled Keywords:	Medical Data Classification; Similarity Measure; Fuzzy Soft Set; Distance Measure.
Subjects:	R Medicine > RC Internal medicine > RC71-78.7 Examination. Diagnosis Including radiography T Technology > T Technology (General)
Divisions:	Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User:	Miss Nur Rasyidah Rosli
Date Deposited:	23 Dec 2021 04:15
Last Modified:	23 Dec 2021 04:15
URI:	http://eprints.uthm.edu.my/id/eprint/4874

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