Improved Fuzzy Modeling of Thyroid Disease Detection using Interval Type-2 Fuzzy Techniques
Prabhash Chandra1, Devendra Agarwal2, Praveen Kumar Shukla3
1Prabhash Chandra*, Research Scholar, Department of Computer Science and Engineering, Babu Banarasi Das University, Lucknow, India.
2Devendra Agarwal, Professor and Head, Department of Computer Science, School of Engineering, BBD University, Lucknow, India.
3Dr. Praveen Kumar Shukla, Professor and Head, Department of Information Technology, Babu Banarasi Das Northern, Institute of Technology, Lucknow, India.
Manuscript received on July 12, 2020. | Revised Manuscript received on July 27, 2020. | Manuscript published on August 30, 2020. | PP: 220-223 | Volume-9 Issue-6, August 2020. | Retrieval Number: C5931029320/2020©BEIESP | DOI: 10.35940/ijeat.C5931.089620
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© 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: Fuzzy Systems are the managers for the modeling environment uncertainty for real time decision making. Type 1 fuzzy systems are much interpretable but less accurate than the type 2 and Interval Type 2 Fuzzy Systems (IT2FS). The paper introduces an experimental analysis to address the interpretability quantification and accuracy measurement in all types of fuzzy implementations. The experiment is carried out on the Thyroid dataset which leads to predict the level of Thyroid in the patients.
Keywords: Fuzzy Modeling, Knowledge Base, Inference, Mamdani Type Fuzzy System.