Estudo e desenvolvimento de métodos para predição de doadores de sangue

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Universidade Federal de Goiás


Hemotherapy units has difficulties to optimize the search for blood donors in emergency situations, as well as to keep their blood stocks at adequate levels. On the other hand, the use of computational techniques for prediction has obtained promissing results in several areas of knowledge, and can be seen as a fundamental tool in obtaining blood donations, however, are little explored in this sector. Given this gap, this research aimed to analyze and develop prediction techniques to optimize the search for donors with higher conversion rate to the donation, focusing on data mining techniques. For this, we first analyzed the performance of traditional literature classifiers applied to a real database, which produced unsatisfactory prediction results. Seeking for higher quality results we propose a top-k recommendation approach of blood donors, which uses heuristics to estimate a confidence degree in donation. Computational experiments show that the top-k recommendation approach achieves good results for all three developed heuristics. The support vector-based heuristic achieving 94.09% of precision among the top-10 recommended, and 99.90% of precision for top-1, for the same data set that the classifiers were not successful. It is expected that the results of this research will contribute to the academic community due to the variety of classifiers analyzed and especially due to the proposed top-k recommendations approach. In the future, this approach can be better analyzed with other databases and even improved by the development of new heuristics. In addition, it is believed that the developed top-k approach can be used in health prediction systems, with a focus on predicting blood donors, especially in emergency situations.



Classificadores, Doação de sangue, Sistemas de recomendação, Blood donation, Classifiers, Recommendation systems


SILVA, F. H. Estudo e desenvolvimento de métodos para predição de doadores de sangue. 2018. 80 f. Dissertação (Mestrado em Modelagem e Otimização) - Universidade Federal de Goiás, Catalão, 2018.