Blood Diseases Detection using Classical Machine Learning Algorithms
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 7
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.
Authors and Affiliations
Fahad Kamal Alsheref, Wael Hassan Gomaa
Keywords
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- EP ID EP611208
- DOI 10.14569/IJACSA.2019.0100712
- Views 74
- Downloads 0
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