Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Classification method using fuzzy level set subgrouping
Expert Systems with Applications: An International Journal
A comparative study on thyroid disease diagnosis using neural networks
Expert Systems with Applications: An International Journal
An immune inspired co-evolutionary affinity network for prefetching of distributed object
Journal of Parallel and Distributed Computing
An automatic diagnosis system based on thyroid gland: ADSTG
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Designing an artificial immune system-based machine learning classifier for medical diagnosis
ICICA'10 Proceedings of the First international conference on Information computing and applications
An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A Three-Stage Expert System Based on Support Vector Machines for Thyroid Disease Diagnosis
Journal of Medical Systems
An expert system for optimising thyroid disease diagnosis
International Journal of Computational Science and Engineering
A Computer Aided Diagnosis System for Thyroid Disease Using Extreme Learning Machine
Journal of Medical Systems
Fuzzy and hard clustering analysis for thyroid disease
Computer Methods and Programs in Biomedicine
A New Hybrid Case-Based Reasoning Approach for Medical Diagnosis Systems
Journal of Medical Systems
Hi-index | 12.06 |
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.