عنوان مقاله [English]
Blood donation has an important and critical role to preserve the health and survival of human life. In today's world, despite the enormous scientific advancements and the great developments in medical sciences, adequate supply of healthy blood is one of the challenges and concerns of the medical community in the world. Preserving and supplying the volume of blood required in blood banks of each region, and the diverse blood groups with the connections between them, with assuming that the number of blood groups are rarer; makes the prediction and planning of blood donation more and more complicated and important during the time. The use of data mining in hospitals and blood transfer centers databases helps in the discovery of relations, so that they can have a future prediction based on the past information. Accordingly, they have better diagnosed and successful cure various illnesses and show the patterns of new injuries. In this paper, we try to use data mining and machine learning techniques in decision making levels at mentioned field, to use this mechanism for prediction that how much blood will be donate to blood transfusion centers and blood banks in different period time, to estimate and supply the required blood volume of blood banks in different areas. In this regard, we use several classification algorithms in supervised learning for the prediction, including decision tree algorithms, KNN, SVM and MLP, these algorithms are implemented to predict and results of accuracy are presented.
ـ Aghighi, Farzaneh; Hossein Aghighi and Omid Mehdi ebabati. (2017). " Evaluation of the efficiency of SVM and KNN Classification algorithms to extract urban effects from LiDAR cloud points", Second International Conference on Knowledge-based Research in Computer Engineering & Information Technology, Tehran, Majlisi University. (in persian)
ـ Akben, S. B. (2018). Early Stage Chronic Kidney Disease Diagnosis by Applying Data Mining Methods to Urinalysis, Blood Analysis and Disease History. IRBM, 39(5), 353-358.
ـ Ashoori, M., Alizade, S., Eivary, H. S. H., Rastad, S., & Eivary, S. S. H. (2015). A model to predict the sequential behavior of healthy blood donors using data mining. Journal of Research & Health, 5(2), 141-148.
ـ Bahel, D., Ghosh, P., Sarkar, A., & Lanham, M. A. (2017). Predicting Blood Donations Using Machine Learning Techniques. In CONFERENCE PROCEEDINGS BY TRACK (p. 323).
ـ Balakrishnan, J. M. D. (2010). Significance of classification Techniques in prediction of Learning disabilities. arXiv preprint arXiv:1011.0628.
ـ Bhardwaj, A., Sharma, A., & Shrivastava, V. K. (2012). Data mining techniques and their implementation in blood bank sector–a review. International Journal of Engineering Research and Applications (IJERA), 2(4), 1303-1309.
ـ Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford university press.
ـ Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Wadsworth and Brooks. Cole Statistics/Probability Series.
ـ Brunassi, L. D. A., Moura, D. J. D., Nääs, I. D. A., Vale, M. M. D., Souza, S. R. L. D., Lima, K. A. O. D., ... & Bueno, L. G. D. F. (2010). Improving detection of dairy cow estrus using fuzzy logic. Scientia Agricola, 67(5), 503-509.
ـ Cardoso, H. F. (2008). Sample-specific (universal) metric approaches for determining the sex of immature human skeletal remains using permanent tooth dimensions. Journal of Archaeological Science, 35(1), 158-168.
ـ Chang, H. H., & Tsay, S. F. (2004). Integrating of SOM and K-mean in data mining clustering: An empirical study of CRM and profitability evaluation.
ـ Darwiche, M., Feuilloy, M., Bousaleh, G., & Schang, D. (2010, May). Prediction of blood transfusion donation. In 2010 Fourth International Conference on Research Challenges in Information Science (RCIS) (pp. 51-56). IEEE.
ـ Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification. John Wiley & Sons.
ـ Elmamouz, G. and M. Nadimi. (2012). A review of methods for prediction of type 2 diabetes based on Bayesian theory. National Conference on Science and Computer Engineering.
ـ Fazli H, Momeni H. (2013). Comparison and evaluation of data mining algorithms, decision tree and SVM application for intrusion detection. In: Proceedings of 8th Symposium progress in science and technology 2013, Mashhad. Iran.
ـ Ghritlahre, H. K., & Prasad, R. K. (2018). Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique. Journal of environmental management, 223, 566-575.
ـ Goldschmidt, R., & Passos, E. (2005). Data mining: um guia prático. Gulf Professional Publishing.
ـ Grilli, E., Menna, F., & Remondino, F. (2017). A review of point clouds segmentation and classification algorithms. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 339.
ـ Hughes, A. M. (1994). Strategic database marketing. IL: Probus Publishing Company.
ـ Khamis, H. S., Cheruiyot, K. W., & Kimani, S. (2014). Application of k-nearest neighbour classification in medical data mining. International Journal of Information and Communication Technology Research, 4(4).
ـ Khomri, Neda and Hadi Rainani. (2018). "Data mining, concepts and applications (Electronic City)", Second International Conference on Electrical Engineering, Computer Science and Information Technology, Hamedan. (in persian)
ـ Lambda, A., & Kumar, D. (2016). Survey on KNN and Its Variants. International Journal of Advanced Research in Computer and Communication Engineering, 5(5).
ـ Mendez-Santiago, J., & Teja, A. S. (2000). Solubility of solids in supercritical fluids: consistency of data and a new model for cosolvent systems. Industrial & Engineering Chemistry Research, 39(12), 4767-4771.
ـ Mostafa, M. M. (2009). Profiling blood donors in Egypt: A neural network analysis. Expert Systems with Applications, 36(3), 5031-5038.
ـ Nowruzi Tiolla, Sare; Morteza Mousavi and Manouchehr Kazemi. (2017). "Intrusion Detection Using Combined Clustering and Knn Algorithm", Fourth National Conference on Information Technology, Computer and Telecommunications, Mashhad, Torbat Heydarieh University. (in persian)
ـ Quinlan, J. R. (1993). Program for machine learning. C4. 5.
ـ Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81-106.
ـ Reinartz, W. J., & Kumar, V. (2000). On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of marketing, 64(4), 17-35.
ـ Richards, J. A., & Richards, J. A. (1999). Remote sensing digital image analysis (Vol. 3, pp. 10-38). Berlin et al.: Springer.
ـ Shakiba, Zeinab; Mahdieh Khedri and Faeghe Faghih Mousavi. (2017) "The performance Comparison of KNN and SVM Algorithms in Categorization of Texts", Fourth International Conference on Knowledge Based Research in Computer Engineering and Information Technology, Tehran, University of Abrar. (in persian)
ـ Sparks, D. L., Hernandez, R., & Estévez, L. A. (2008). Evaluation of density-based models for the solubility of solids in supercritical carbon dioxide and formulation of a new model. Chemical Engineering Science, 63(17), 4292-4301.
ـ Testik, M. C., Ozkaya, B. Y., Aksu, S., & Ozcebe, O. I. (2012). Discovering blood donor arrival patterns using data mining: A method to investigate service quality at blood centers. Journal of medical systems, 36(2), 579-594.
ـ Tharwat, A., Ghanem, A. M., & Hassanien, A. E. (2013, December). Three different classifiers for facial age estimation based on k-nearest neighbor. In 2013 9th International Computer Engineering Conference (ICENCO) (pp. 55-60). IEEE.
ـ Trabelsi, A., Elouedi, Z., & Lefevre, E. (2018). Decision tree classifiers for evidential attribute values and class labels. Fuzzy Sets and Systems.
ـ van Eck, N. J., & van Wezel, M. (2008). Application of reinforcement learning to the game of Othello. Computers & Operations Research, 35(6), 1999-2017.
ـ Yeh, I. C., Yang, K. J., & Ting, T. M. (2009). Knowledge discovery on RFM model using Bernoulli sequence. Expert Systems with Applications, 36(3), 5866-5871.
ـ Yu, P. L. H., Chung, K. H., Lin, C. K., Chan, J. S. K., & Lee, C. K. (2007). Predicting potential drop‐out and future commitment for first‐time donors based on first 1· 5‐year donation patterns: the case in Hong Kong Chinese donors. Vox sanguinis, 93(1), 57-63