Diabetes Diagnosis System Based on Support Vector Machines Trained by Vortex Optimization Algorithm


ÇANKAYA Ş. F. , ÇANKAYA İ. A. , YİĞİT T. , KOYUN A.

NATURE-INSPIRED INTELLIGENT TECHNIQUES FOR SOLVING BIOMEDICAL ENGINEERING PROBLEMS, ss.203-218, 2018 (SCI İndekslerine Giren Dergi) identifier

  • Cilt numarası:
  • Basım Tarihi: 2018
  • Doi Numarası: 10.4018/978-1-5225-4769-3.ch009
  • Dergi Adı: NATURE-INSPIRED INTELLIGENT TECHNIQUES FOR SOLVING BIOMEDICAL ENGINEERING PROBLEMS
  • Sayfa Sayıları: ss.203-218

Özet

Artificial intelligence is widely enrolled in different types of real-world problems. In this context, developing diagnosis-based systems is one of the most popular research interests. Considering medical service purposes, using such systems has enabled doctors and other individuals taking roles in medical services to take instant, efficient expert support from computers. One cannot deny that intelligent systems are able to make diagnosis over any type of disease. That just depends on decision-making infrastructure of the formed intelligent diagnosis system. In the context of the explanations, this chapter introduces a diagnosis system formed by support vector machines (SVM) trained by vortex optimization algorithm (VOA). As a continuation of previously done works, the research considered here aims to diagnose diabetes. The chapter briefly gives information about details of the system and findings reached after using the developed system.