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  • 标题:Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
  • 本地全文:下载
  • 作者:Utku Kose ; Gur Emre Guraksin ; Omer Deperlioglu
  • 期刊名称:Brain. Broad Research in Artificial Intelligence and Neuroscience
  • 印刷版ISSN:2067-3957
  • 出版年度:2016
  • 卷号:7
  • 期号:1
  • 页码:80-90
  • 语种:English
  • 出版社:EduSoft publishing
  • 摘要:The definition, diagnosis and classification of Diabetes Mellitus and its complications are very important. First of all, the World Health Organization (WHO) and other societies, as well as scientists have done lots of studies regarding this subject. One of the most important research interests of this subject is the computer supported decision systems for diagnosing diabetes. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics to streamline the diagnostic process in daily routine and avoid misdiagnosis. In this study, a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM) and Cognitive Development Optimization Algorithm (CoDOA) has been proposed. Along the training of SVM, CoDOA was used for determining the sigma parameter of the Gauss (RBF) kernel function, and eventually, a classification process was made over the diabetes data set, which is related to Pima Indians. The proposed approach offers an alternative solution to the field of Artificial Intelligence-based diabetes diagnosis, and contributes to the related literature on diagnosis processes.
  • 其他摘要:The definition, diagnosis and classification of Diabetes Mellitus and its complications are very important. First of all, the World Health Organization (WHO) and other societies, as well as scientists have done lots of studies regarding this subject. One of the most important research interests of this subject is the computer supported decision systems for diagnosing diabetes. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics to streamline the diagnostic process in daily routine and avoid misdiagnosis. In this study, a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM) and Cognitive Development Optimization Algorithm (CoDOA) has been proposed. Along the training of SVM, CoDOA was used for determining the sigma parameter of the Gauss (RBF) kernel function, and eventually, a classification process was made over the diabetes data set, which is related to Pima Indians. The proposed approach offers an alternative solution to the field of Artificial Intelligence-based diabetes diagnosis, and contributes to the related literature on diagnosis processes.
  • 关键词:diagnosis diabetes; support vector machines; cognitive development optimization algorithm; classification; pimaindians diabetes
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