Citation

Ramanathan, Thirumalaimuthu Thirumalaiappan and Hossen, Md. Jakir (2025) Incremental learning based fuzzy reasoning approach for diagnosis of thyroid disease. Bulletin of Electrical Engineering and Informatics, 14 (4). pp. 2793-2806. ISSN 2089-3191

Text Incremental learning based fuzzy reasoning approach for diagnosis of thyroid disease _ Ramanath _ Bulletin of Electrical Engineering and Informatics.pdf - Published Version Restricted to Repository staff only Download (7MB)

Abstract

This paper presents a novel hybrid fuzzy logic approach for the classification of thyroid disease. Hybrid fuzzy logic approaches have brought many benefits to the medical data classification problems such as reasoning on uncertain or incomplete data. The machine learning algorithms had been used with the fuzzy expert systems to define the fuzzy rule base. The optimization techniques had been used in the fuzzy expert systems for optimizing the fuzzy membership functions and fuzzy rules. Enhancing the machine learning algorithms and optimization techniques that are integrated with the fuzzy logic method can improve the overall performance of the fuzzy expert system. To deal with the curse of dimensionality problem and to enhance the integration of machine learning algorithm and fuzzy logic method, this paper presents an incremental learning based parallel fuzzy reasoning system (IL-PFRS) for medical diagnosis. In this research work, the decision tree classifier is used to extract the features from dataset. IL-PFRS is applied to classify the thyroid disease which is serious disease that needs attention and earlier detection. The thyroid disease dataset obtained from the UCI machine learning repository is used in this research work where the IL-PFRS showed the classification accuracy of 99% when testing using this dataset.

Item Type:	Article
Uncontrolled Keywords:	Fuzzy logic
Subjects:	Q Science > QA Mathematics > QA1-43 General
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	27 Aug 2025 05:02
Last Modified:	27 Aug 2025 05:02
URII:	http://shdl.mmu.edu.my/id/eprint/14461

Downloads

Edit (login required)