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Proceedings Paper

Prediction of healthy blood with data mining classification by using Decision Tree, Naive Baysian and SVM approaches
Author(s): Mahdieh Khalilinezhad; Behrooz Minaei; Gianni Vernazza; Silvana Dellepiane
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Paper Abstract

Data mining (DM) is the process of discovery knowledge from large databases. Applications of data mining in Blood Transfusion Organizations could be useful for improving the performance of blood donation service. The aim of this research is the prediction of healthiness of blood donors in Blood Transfusion Organization (BTO). For this goal, three famous algorithms such as Decision Tree C4.5, Naïve Bayesian classifier, and Support Vector Machine have been chosen and applied to a real database made of 11006 donors. Seven fields such as sex, age, job, education, marital status, type of donor, results of blood tests (doctors’ comments and lab results about healthy or unhealthy blood donors) have been selected as input to these algorithms. The results of the three algorithms have been compared and an error cost analysis has been performed. According to this research and the obtained results, the best algorithm with low error cost and high accuracy is SVM. This research helps BTO to realize a model from blood donors in each area in order to predict the healthy blood or unhealthy blood of donors. This research could be useful if used in parallel with laboratory tests to better separate unhealthy blood.

Paper Details

Date Published: 4 March 2015
PDF: 10 pages
Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94432G (4 March 2015); doi: 10.1117/12.2179871
Show Author Affiliations
Mahdieh Khalilinezhad, Univ. degli Studi di Genova (Italy)
Behrooz Minaei, Univ. of Science and Technology (Iran, Islamic Republic of)
Gianni Vernazza, Univ. degli Studi di Genova (Italy)
Silvana Dellepiane, Univ. degli Studi di Genova (Italy)


Published in SPIE Proceedings Vol. 9443:
Sixth International Conference on Graphic and Image Processing (ICGIP 2014)
Yulin Wang; Xudong Jiang; David Zhang, Editor(s)

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