Diabetes Determination via Vortex Optimization Algorithm Based Support Vector Machines

Medical Technologies National Conference (TIPTEKNO) -- OCT 15-18, 2015 -- Bodrum, TURKEYWOS: 000380505200096Approaches performed based on computer supported systems within the medical field gain more popularity day by day. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics. Diabetes is one of these diseases. In this study, a diabetes diagnosis system based on Support Vector Machines has been proposed. Along training of SVM, Vortex Optimization Algorithm was used for determining the sigma parameter of the Gauss (RBF) kernel function, and a classification process has been done over the diabetes data set related to Pima Indians

Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes

WOS: 000376690700008The 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

BRAIN Journal - Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes

<div><i>Abstract </i></div><div><br></div>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.<div><br></div><div><b>Find more at:</b></div><div><b>https://www.edusoft.ro/brain/index.php/brain/article/view/580</b><br></div