Application of Neuro Fuzzy-Rule Based Classifier in Thyroid Disease Diagnosis
Siddhi Vinod Parodkar1, Amita Dessai2
1Ms. Siddhi Vinod Parodkar, Department of Electronics and Telecommunication, Goa College of Engineering, Farmagudi, India.
2Ms. Amita Dessai, Assistant Professor, Department of Electronics and Telecommunication, Goa College of Engineering, Farmagudi, India.
Manuscript received on 20 March 2017 | Revised Manuscript received on 30 March 2017 | Manuscript published on 30 March 2017 | PP: 13-19 | Volume-6 Issue-1, March 2017 | Retrieval Number: A1656036117©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Thyroid diseases are rising at an alarming rate worldwide across several age groups. Current practice for thyroid disease diagnosis is doctor’s examination and a number of blood tests which requires lots of experience and knowledge. Because of the difficulty in considering large number of interrelated measurements and non-specific nature of symptoms of a disease, experts often have imprecise knowledge which causes uncertainty in diagnosis procedure. Thyroid disease detection via proper interpretation of the thyroid data is an important classification problem. The main objective of this project is to design an intelligent system that can diagnose the thyroid diseases with minimum diagnosis time and enhanced diagnosis accuracy.
Keyword: Feature Selection (FS), Linguistic Hedge (LH), Neuro-Fuzzy Classifier (NFC), Scaled Conjugate Gradient algorithm (SCG), Thyroid Disorders.
Scope of the Article: Application Specific ICs (ASICs)