A Survey on various Machine Learning Approaches for thalassemia detection and classification

Abstract

Thalassemia is a genetic blood disease caused by a deficiency in the production of hemoglobin, the central protein found in red blood cells and responsible for carrying oxygen from the lungs to the parts of the body. Hemoglobin consists of two types of protein chains, two alpha chains and two beta chains. In diagnosing thalassemia, doctors rely on two types of tests: a complete blood count (CBC) and a special hemoglobin test (Hemoglobin Electrophoresis). A complete blood count, or CBC, measures the amount of hemoglobin and various types of blood cells, such as red blood cells, in a blood sample. In this study present a survey of different method based on artificial intelligence to classify and detection thalassemia using the variable (parameter) of the CBC test which include RBC, HGB, MCV, HTC, HB . To distinguish between thalassemia minor alpha and thalassemia major beta patients. Decision tree, Naïve Bayes, support vector machine (SVM), and neural network classification method are used.