Medical Diagnosis with a Novel SVM-CoDOA Based Hybrid Approach
This is an incremental improvement for medical diagnosis applications.
The study tackled medical diagnosis by introducing a novel SVM-CoDOA hybrid system, which combines Support Vector Machines with a newly developed Cognitive Development Optimization Algorithm, and reported effectiveness in this domain.
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.