Thyroid Disease Diagnosis Based on Genetic Algorithms using PNN and SVM

摘要：

Thyroid gland produces thyroid hormones to help the regulation of the bodys metabolism. The abnormalities of producing thyroid hormones are divided into two categories. Hypothyroidism which is related to production of insufficient thyroid hormone and hyperthyroidism related to production of excessive thyroid hormone. Separating these two diseases is very important for thyroid diagnosis. Therefore Support Vector Machines and Probabilistic Neural Network are proposed to classification. These methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper feature selection is argued as an important problem via diagnosis and demonstrate that GAs provide a simple, general and powerful framework for selecting good subsets of features leading to improved diagnosis rates. Thyroid disease datasets are taken from UCI machine learning dataset.

关键词： Genetic algorithms Probabilistic Neural Network Support Vector Machine Thyroid disease diagnosis

作者: Fatemeh Saiti Afsaneh Alavi Naini Mahdi Aliyari shoorehdeli Mohammad Teshnehlab

作者单位: Biomedical Engineering Group,Electrical Engineering Department,K.N.Toosi University of Technology,Te Mechatronics Department,Science and research branch Islamic Azad University,Tehran,Iran

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）