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Using Knowledge Discovery to Enhance Classification Techniques for Detect Malaria-Infected Red Blood Cells

J. A. Alkrimi1 , Sh. A. Toma2 , R. S. Mohammed3 , L. E. George4

1 College of Dentistry, University of Babylon, Babylon, Iraq.
2 College of Medicine, Baghdad University, Baghdad, Iraq.
3 Al Mansur Institute of Medical Technology, Middle Technical University, Baghdad.
4 Department of Computer Science, College of Sciences, Baghdad University, Iraq.

Section:Research Paper, Product Type: Journal
Vol.8 , Issue.1 , pp.1-6, Feb-2020

Online published on Feb 28, 2020


Copyright © J. A. Alkrimi, Sh. A. Toma, R. S. Mohammed, L. E. George . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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IEEE Style Citation: J. A. Alkrimi, Sh. A. Toma, R. S. Mohammed, L. E. George, “Using Knowledge Discovery to Enhance Classification Techniques for Detect Malaria-Infected Red Blood Cells,” International Journal of Scientific Research in Network Security and Communication, Vol.8, Issue.1, pp.1-6, 2020.

MLA Style Citation: J. A. Alkrimi, Sh. A. Toma, R. S. Mohammed, L. E. George "Using Knowledge Discovery to Enhance Classification Techniques for Detect Malaria-Infected Red Blood Cells." International Journal of Scientific Research in Network Security and Communication 8.1 (2020): 1-6.

APA Style Citation: J. A. Alkrimi, Sh. A. Toma, R. S. Mohammed, L. E. George, (2020). Using Knowledge Discovery to Enhance Classification Techniques for Detect Malaria-Infected Red Blood Cells. International Journal of Scientific Research in Network Security and Communication, 8(1), 1-6.

BibTex Style Citation:
@article{Alkrimi_2020,
author = {J. A. Alkrimi, Sh. A. Toma, R. S. Mohammed, L. E. George},
title = {Using Knowledge Discovery to Enhance Classification Techniques for Detect Malaria-Infected Red Blood Cells},
journal = {International Journal of Scientific Research in Network Security and Communication},
issue_date = {2 2020},
volume = {8},
Issue = {1},
month = {2},
year = {2020},
issn = {2347-2693},
pages = {1-6},
url = {https://www.isroset.org/journal/IJSRNSC/full_paper_view.php?paper_id=380},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.isroset.org/journal/IJSRNSC/full_paper_view.php?paper_id=380
TI - Using Knowledge Discovery to Enhance Classification Techniques for Detect Malaria-Infected Red Blood Cells
T2 - International Journal of Scientific Research in Network Security and Communication
AU - J. A. Alkrimi, Sh. A. Toma, R. S. Mohammed, L. E. George
PY - 2020
DA - 2020/02/28
PB - IJCSE, Indore, INDIA
SP - 1-6
IS - 1
VL - 8
SN - 2347-2693
ER -

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Abstract :
Malaria is one of the three most serious diseases worldwide, affecting millions each year, mainly in the tropics where the most serious illnesses are caused by Plasmodium falciparum. The aim of this research paper is to enhance the main machine-learning classification algorithms that used for malaria-infected red blood cells (MRBCs) and evaluation the classification model accuracy. This study uses knowledge discovery technique to analyses the blood smear images. The system that determines the computerized methods of image analysis generally involves three main phases. Firstly, data collection, pre-processing and feature extraction are conducted based on the characteristics of normal and MRBCs. Secondly, application knowledge discovery process to extracts high quality information of normal and MRBCs. Thirdly, using prediction model of classification machine learning algorithms to classify 1000 RBCs sample. After that, use ten-fold cross-validation to evaluation overfitting model and the confusion matrix to evaluate the performance of a classification model. The results indicate that the algorithms achieve high accuracy more than 92.3%. Also, obtain high prediction 90.8%, reliability 92% and ability to distinguish positive and negative classification model 93%. In addition, the reduction in time build the model was very clearly, 13.6 second and 5.8 times faster respectively.

Key-Words / Index Term :
knowledge discovery, machine learning classification algorithms, feature extraction and feature redaction, red blood cells

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