Paper Info
Title
A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis
Abstract
Proper interpretation of the thyroid gland functional data is an important issue in the diagnosis of thyroid disease. The primary role of the thyroid gland is to help regulation of the body's metabolism. Thyroid hormone produced by the thyroid gland provides this. Production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism) defines the type of thyroid disease. Artificial immune systems (AISs) is a new but effective branch of artificial intelligence. Among the systems proposed in this field so far, artificial immune recognition system (AIRS), which was proposed by A. Watkins, has shown an effective and intriguing performance on the problems it was applied. This study aims at diagnosing thyroid disease with a new hybrid machine learning method including this classification system. By hybridizing AIRS with a developed Fuzzy weighted pre-processing, a method is obtained to solve this diagnosis problem via classifying. The robustness of this method with regard to sampling variations is examined using a cross-validation method. We used thyroid disease dataset which is taken from UCI machine learning respiratory. We obtained a classification accuracy of 85%, which is the highest one reached so far. The classification accuracy was obtained via a 10-fold cross-validation.
Year
DOI
Venue
2007
10.1016/j.eswa.2006.02.007
Expert Syst. Appl.
Keywords
Field
DocType
fuzzy weighted pre-processing,thyroid disease,classification accuracy,thyroid disease diagnosis,thyroid disease dataset,thyroid hormone,artificial intelligence,thyroid gland functional data,novel hybrid method,artificial immune recognition system,artificial immune recognition system (airs),cross-validation method,artificial immune system,artificial immune systems,thyroid gland,cross validation,classification system,artificial intelligent,machine learning
Hormone,Artificial immune system,Recognition system,Computer science,Fuzzy logic,Thyroid,Robustness (computer science),Immune system,Artificial intelligence,Machine learning,Thyroid disease
Journal
Volume
Issue
ISSN
32
4
Expert Systems With Applications
Citations 
PageRank 
References 
29
1.45
2
Authors
3
Name
Order
Citations
PageRank
Kemal Polat1134897.38
Seral Şahan221017.86
Salih Güneş3126778.53