  Machine Learning is an important sub-field of the Artificial Intelligence and
it has been become a very critical task to train Machine Learning techniques
via effective method or techniques. Recently, researchers try to use
alternative techniques to improve ability of Machine Learning techniques.
Moving from the explanations, 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, use of optimization algorithms is an essential task to train
and improve Machine Learning techniques. In this sense, the study has provided
a medical diagnosis oriented problem scope in order to show effectiveness of
the SVM-CoDOA hybrid formation.
