Cell Disease Identification Using Enhanced Salp Swarm Algorithm in Machine Learning

Abstract

Computerized diagnosis of white blood cells cancer diseases for instance Leukemia and Myeloma is a demanding biomedical research area. Our method presents for the first time a new state of the application that assists in diagnosing the white blood cells diseases. We divide these diseases into two types; each type includes similar symptoms a disease that may my method is applied on one of the two diseases category by computing different features. Finally, Random Forest classifier is enforced for final decision. The proposed method aims to early discovery of white blood cells cancer, reduce the misdiagnosis cases in adding up to develop the system learning methodology. Moreover, allowing the experts only to have the final modification on the result obtained from the system. The proposed approach achieved an accuracy of 93% in the first type and 95% in the second type.