%0 Journal Article
	%A Jameela Ali Alkrimi and  Hamid A. Jalab and  Loay E. George and  Abdul Rahim Ahmad and  Azizah Suliman and  Karim Al-Jashamy
	%D 2015
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 97, 2015
	%T Comparative Study Using Weka for Red Blood Cells Classification
	%U https://publications.waset.org/pdf/10000202
	%V 97
	%X Red blood cells (RBC) are the most common types of
blood cells and are the most intensively studied in cell biology. The
lack of RBCs is a condition in which the amount of hemoglobin level
is lower than normal and is referred to as “anemia”. Abnormalities in
RBCs will affect the exchange of oxygen. This paper presents a
comparative study for various techniques for classifying the RBCs as
normal or abnormal (anemic) using WEKA. WEKA is an open
source consists of different machine learning algorithms for data
mining applications. The algorithms tested are Radial Basis Function
neural network, Support vector machine, and K-Nearest Neighbors
algorithm. Two sets of combined features were utilized for
classification of blood cells images. The first set, exclusively consist
of geometrical features, was used to identify whether the tested blood
cell has a spherical shape or non-spherical cells. While the second
set, consist mainly of textural features was used to recognize the
types of the spherical cells. We have provided an evaluation based on
applying these classification methods to our RBCs image dataset
which were obtained from Serdang Hospital - Malaysia, and
measuring the accuracy of test results. The best achieved
classification rates are 97%, 98%, and 79% for Support vector
machines, Radial Basis Function neural network, and K-Nearest
Neighbors algorithm respectively.

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