Abstract
In the last decades, with the increase in the amount of data stored in the healthcare industry, it is also extended the possibility of obtaining important information to support the decision-making process of health professionals. This article has as evidence to apply Data Mining (DM) techniques to health databases of patients with medical Deep Vein Thrombosis (DVT) risk, with the objective of classifying, based on different attributes obtained in medical discharge reports, the main prophylactic measures taken. Therefore, to achieve this goal, the free software Weka was used aiming to facilitate the process of DM, along with the algorithms chosen. In view of this, it was concluded that the service to which each patient is associated is the most relevant factor for prophylactic measures followed by the age range to which the patient belongs. This study also deduces that it can be possible to obtain classifiers capable of predicting the best prophylactic measures with a qualitative level similar as one of a health professional and, thereafter, it can be possible to obtain the classification.
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References
Pitta, G.B.B., Gomes, R.R.: A frequência da utilização de profilaxia para trombose venosa profunda em pacientes clínicos hospitalizados. J. Vasc. Bras. 9(4), 220–228 (2010)
Machado, N.L.B., Leite, T.L., Pitta, G.B.B.: Frequência da profilaxia mecânica para trombose venosa profunda em pacientes internados em uma unidade de emergência de Maceió. J. Vasc. Bras. 7(4), 333–340 (2008)
Correia, A., Winck, J.C.: Trombose venosa profunda e Embolismo Pulmonar | Programa Harvard Medical School Portugal. Programa Harvard Medical School Portugal (2011). https://hmsportugal.wordpress.com/2011/03/28/trombose-venosa-profunda-e-embolismo-pulmonar/. Accessed 6 Nov 2018
Ramalho, V.V.: Modelo de data mining para deteção de embolias pulmonares. Instituto Superior de Engenharia de Lisboa (2013)
Oliveira, S., et al.: Clustering Data Mining models to identify patterns in weaning patient failures. Int. J. Biol. Biomed. Eng. 10, 183–190 (2016)
Neves, J., et al.: A deep-big data approach to health care in the AI age. Mob. Networks Appl. 23(4), 1123–1128 (2018)
Rodrigues, M., Peixoto, H., Esteves, M., Machado, J., Abelha, A.: Understanding stroke in dialysis and chronic kidney disease. Procedia Comput. Sci. 113(4), 591–596 (2017)
Miguel da Silva Ferreira, P.: Aplicação de Algoritmos de Aprendizagem Automática para a Previsão de Cancro de Mama. Faculdade de Ciências da Universidade do Porto (2010)
dos S. Lima, T.: Estudo Comparativo dos Algoritmos de Classificação da Ferramenta WEKA. Centro Universitário Luterano de Palmas (2005)
Clemente, V., de Noronha Rocha, T.H., Gargano Lemos Rosewarne, T., Rocha, R., César Lopes Vaz, J., Pinto Espíndola, R.: Proposta de Gestão da Trombose Venosa Profunda Através de Mineração de Dados. Revista Acreditação: ACRED. (Ejemplar dedicado a: Revista Acreditação), vol. 3, no. 5, [s.n.], pp. 34–38 (2013). ISSN-e 2237-5643
Aljumah, A.A., Ahamad, M.G., Siddiqui, M.K.: Application of data mining: diabetes health care in young and old patients. J. King Saud Univ. Comput. Inf. Sci. 25(2), 127–136 (2013)
Bhatla, N., Jyoti, K.: An analysis of heart disease prediction using different data mining techniques. Int. J. Eng. Res. Tecnol. 1(8), 1–4 (2012)
de M. Nogueira, R.: Análise dos Impactos Harmônicos em uma Indústria de Manufatura de Eletroeletrônicos utilizando Árvores de Decisão. Universidade Federal do Pará, Instituto de Tecnologia (2015)
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This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.
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Cruz, M., Esteves, M., Peixoto, H., Abelha, A., Machado, J. (2019). Application of Data Mining for the Prediction of Prophylactic Measures in Patients at Risk of Deep Vein Thrombosis. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) New Knowledge in Information Systems and Technologies. WorldCIST'19 2019. Advances in Intelligent Systems and Computing, vol 932. Springer, Cham. https://doi.org/10.1007/978-3-030-16187-3_54
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DOI: https://doi.org/10.1007/978-3-030-16187-3_54
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