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DOI: 10.14569/IJACSA.2019.0100712
PDF

Blood Diseases Detection using Classical Machine Learning Algorithms

Author 1: Fahad Kamal Alsheref
Author 2: Wael Hassan Gomaa

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 7, 2019.

  • Abstract and Keywords
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Abstract: Blood analysis is an essential indicator for many diseases; it contains several parameters which are a sign for specific blood diseases. For predicting the disease according to the blood analysis, patterns that lead to identifying the disease precisely should be recognized. Machine learning is the field responsible for building models for predicting the output based on previous data. The accuracy of machine learning algorithms is based on the quality of collected data for the learning process; this research presents a novel benchmark data set that contains 668 records. The data set is collected and verified by expert physicians from highly trusted sources. Several classical machine learning algorithms are tested and achieved promising results.

Keywords: Machine learning; classification algorithms; decision trees; KNN; k-means; blood disease

Fahad Kamal Alsheref and Wael Hassan Gomaa, “Blood Diseases Detection using Classical Machine Learning Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 10(7), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100712

@article{Alsheref2019,
title = {Blood Diseases Detection using Classical Machine Learning Algorithms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100712},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100712},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {7},
author = {Fahad Kamal Alsheref and Wael Hassan Gomaa}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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