Mechanized analysis of white platelets malignant growth infections, for example, Leukemia and Myeloma is a difficult biomed-ical inquire about point. Our methodology introduces out of the blue another best in class application that helps with diagnosing the white platelets infections. we break these sicknesses into two classifications, every classification contains like side effects infections that may confound in diagnosing. In light of the specialist's determination, one of two methodologies is actualized. Each methodology is connected on one of the two maladies classification by processing distinctive highlights. At last, Random Forest classifier is connected for ultimate choice. The proposed methodology means to early disclosure of white platelets malignancy, decrease the misdiagnosis cases notwithstanding improve the framework learning approach. In addition, permitting the specialists just to have the last tuning on the outcome acquired from the framework. The proposed methodology accomplished an exactness of 93% in the principal classification and 95% in the second class.