An Efficient Strategy for Blood Diseases Detection Based on Grey Wolf Optimization as Feature Selection and Machine Learning Techniques

Abstract

Acute Lymphoblastic Leukemia (ALL) is a cancer that infects the blood cells causing the development of lymphocytes in large numbers. Diagnostic tests are costly and very time-consuming. It is important to diagnose ALL using Peripheral Blood Smear (PBS) images, especially in the initial screening cases. Several issues affect the examination process such as diagnostic error, symptoms, and nonspecific nature signs of ALL. Therefore, the objective of this study is to enforce machine-learning classifiers in the detection of Acute Lymphoblastic Leukemia as benign or malignant after using the grey wolf optimization algorithm in feature selection. The images have been enhanced by using an adaptive threshold to improve the contrast and remove errors. The model is based on grey wolf optimization technology which has been developed for feature reduction. Finally, acute lymphoblastic leukemia has been classified into benign and malignant using K-nearest neighbors (KNN), support vector machine (SVM), naïve Bayes (NB), and random forest (RF) classifiers. The best accuracy, sensitivity, and specificity of this model were 99.69%, 99.5%, and 99%, respectively, after using the grey wolf optimization algorithm in feature selection. To ensure the effectiveness of the proposed model, comparative results with other classification techniques have been included.

Keywords

• grey wolf optimization
• acute lymphoblastic leukemia
• support vector machine
• random forest
• naïve bayes
• K nearest neighbor