The Use of Data Mining Techniques in the Diagnosis and Prevention of Cerebrovascular Accident (CVA)
DOI:
https://doi.org/10.5753/jidm.2024.3053Keywords:
Stroke, Risk factors, Indicative signs, Data miningAbstract
Over the years, there has been a rise in the occurrence of Cerebrovascular Accident (CVA) cases, due to the increase in the elderly population. Current data indicate that stroke is one of the leading causes of death and disability worldwide, affecting millions of people and leaving survivors with numerous sequelae, whether they are physical or mental. Many factors such as diabetes, smoking, high blood pressure, and others, favor the onset of stroke, which increases mortality rates, making it necessary to know these factors in order to contribute to early preventive measures. In this sense, the purpose of this article is to use six data mining algorithms with the objective of helping to identify and diagnose people prone to having a stroke based on risk factors and indicative signs. The algorithms used were: Decision Tree, K-Nearest Neighbors (K-NN), Multilayer Perceptron Neural Network (MLP), Support Vector Machine (SVM), Naive Bayes, and the Apriori algorithm. The results showed that the MLP and decision tree algorithms obtained the best results, indicating their use in intelligent solutions for this area.
Downloads
References
Azank, F. and Gurgel, G. K. (2020). Dados desbalanceados — o que são e como lidar com eles. [link].
Brasil (1990). Lei 8.069, de 13 de julho de 1990. dispõe sobre o estatuto da criança e do adolescente e dá outras providências. Diário Oficial [da] República Federativa do Brasil.
Brasil, M. d. S. (2013). Diretrizes de atenção à reabilitação da pessoa com acidente vascular cerebral. Campbell, B. C. V. and Khatri, P. (2020). Stroke. The Lancet, 396(10244):129–142. DOI: 10.1016/s0140-6736(20)31179-x.
Cerqueira e Francisco, W. d. (2022). Faixa etária da população brasileira. [link].
de Saúde, S. M. (2021). Hipertensão Arterial: Manejo clínico na Atenção Primária à Saúde, volume 1. Patrícia Aparecida Piva — Gerência técnica de Doenças Renocardiovasculares e Diabetes, 1 edition. Anotação.
Faceli, K., Lorena, A., Gama, J., and Carvalho, A. (2011). Inteligência Artificial–uma abordagem de aprendizado de máquina. Rio de Janeiro: LTC.
Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., et al. (1996). Knowledge discovery and data mining: Towards a unifying framework. In KDD, volume 96, pages 82–88.
Ferrari, D. G. and Castro, L. N. d. (2016). Introdução a mineração de dados. Saraiva Educação SA.
Géron, A. (2019). Mãos à Obra: Aprendizado de Máquina com Scikit-Learn & TensorFlow. Alta Books.
Govindarajan, P., Soundarapandian, R. K., Gandomi, A. H., Patan, R., Jayaraman, P., and Manikandan, R. (2020). Classification of stroke disease using machine learning algorithms. Neural Computing and Applications, 32(3):817–828. DOI: 10.1007/s00521-019-04041-y.
Meschia, J. F., Bushnell, C., Boden-Albala, B., Braun, L. T., Bravata, D. M., Chaturvedi, S., Creager, M. A., Eckel, R. H., Elkind, M. S., Fornage, M., et al. (2014). Guidelines for the primary prevention of stroke: a statement for healthcare professionals from the american heart association/american stroke association. Stroke, 45(12):3754–3832.
Sandercock, P. A., Niewada, M., and Członkowska, A. (2011). The international stroke trial database. Trials, 12(1):1–7.
Santana, R. (2020). Lidando com classes desbalanceadas – machine learning. [link].
Singh, M. S. and Choudhary, P. (2017). Stroke prediction using artificial intelligence. In 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), pages 158–161. DOI: 10.1109/IEME-CON.2017.8079581.
Weka (2022). Class apriori. [link].
Yu, J., Kim, D., Park, H., Chon, S.-c., Cho, K. H., Kim, S.-J., Yu, S., Park, S., and Hong, S. (2019). Semantic analysis of nih stroke scale using machine learning techniques. In 2019 International Conference on Platform Technology and Service (PlatCon), pages 1–5. DOI: 10.1109/Plat-Con.2019.8668961.
Zhang, Y., Song, W., Li, S., Fu, L., and Li, S. (2018). Risk detection of stroke using a feature selection and classification method. IEEE Access, 6:31899–31907. DOI: 10.1109/ACCESS.2018.2833442.