Medical Diagnosis with a Novel SVM-CoDOA Based Hybrid Approach

Hanefi M. Calp

Abstract


Machine Learning is an important sub-field of the Artificial Intelligence and it has become a very critical task to train Machine Learning techniques via effective methods or techniques. Recently, researchers have tried to use alternative techniques to improve the ability of Machine Learning techniques. Moving from the explanations, the objective of this study is to introduce a novel SVM-CoDOA (Cognitive Development Optimization Algorithm trained Support Vector Machines) system for general medical diagnosis. In detail, the system consists of a SVM, which is trained by CoDOA, a newly developed optimization algorithm. As it is known, the use of optimization algorithms is an essential task in training and improving Machine Learning techniques. In this sense, the study has provided a medical diagnosis problem scope in order to show effectiveness of the SVM-CoDOA hybrid formation.


Keywords


Support Vector Machines; Cognitive Development Optimization Algorithm; Machine Learning; Artificial Intelligence; Medical Diagnosis

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