Title: Action fuzzy rule based classifier for analysis of dermatology databases

Authors: T. Deepa; B. Sathiyabhama; J. Akilandeswari; N.P. Gopalan

Addresses: Department of Computer Science and Engineering, Sona College of Technology, Salem, India ' Department of Computer Science and Engineering, Sona College of Technology, Salem, India ' Department of Information Technology, Sona College of Technology, Salem, India ' Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India

Abstract: In healthcare industries, the classification systems are applied to localise primary tumours, prognosis and recurrence of breast cancer, diagnosis of thyroid diseases, and rheumatology. The major requirements for such a system are prediction accuracy and comprehensibility of the learned knowledge by human experts. Success of a classification learning algorithm, in terms of these criteria, is directly related to the scheme used for representing the learned classification knowledge. An Adaptive Neuro Fuzzy Inference System (ANFIS) mechanism is proposed for erythemato-squamous disease analysis and to manage the knowledge acquired and found from the system. In addition, this system utilises Action Rule based Diagnostic System (ARDS) action and fuzzy rule based classifiers in accordance with the severity levels of the diseases. Empirical results endorse that this system is an authentic and reliable tool to reduce the human errors and improve the quality of medical care provided to the public.

Keywords: action rules; fuzzy rules; clinical DSS; decision support systems; medical informatics; knowledge management; fuzzy classifiers; dermatology databases; classification learning; adaptive neuro fuzzy inference system; ANFIS; erythemato-squamous disease analysis; disease severity; neural networks; fuzzy logic.

DOI: 10.1504/IJBET.2014.064828

International Journal of Biomedical Engineering and Technology, 2014 Vol.15 No.4, pp.360 - 379

Received: 16 Nov 2013
Accepted: 30 Apr 2014
Published online: 21 Oct 2014 *

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