Data mining in the diagnosis of hypertension based on National Health Survey 2019

Authors

  • Nicolau Machado de Carvalho PUC Minas
  • Marco Paulo Soares Gomes PUC Minas
  • Luis Enrique Zárate PUC Minas

DOI:

https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1250

Keywords:

Knowledge Discovery, Data Mining, Hypertension

Abstract

Hypertension is a disease that affects a large part of the Brazilian population. As it is a very common disease, some of its risk factors are known, but knowing the order of relevance can bring new insights, especially when the objective is to diagnose the disease. The 2019 National Health Survey was recently made available, which provides new information about the health of the Brazilian population. The objective is to assist in the diagnosis of individuals suffering from Systemic Arterial Hypertension through a method for discovering knowledge and classification by Random Forest. Results achieved an average F1-score of 75%. The conclusions indicate that salt intake, staying outside the ideal weight, not practicing moderate physical activities, and smoking, in that order, are very important factors for diagnosing the disease.

Author Biographies

Nicolau Machado de Carvalho, PUC Minas

Bac., Ciência de Dados, PUC Minas, Belo Horizonte (MG), Brasil

Marco Paulo Soares Gomes, PUC Minas

Dr., Ciência de Dados, PUC Minas, Belo Horizonte (MG), Brasil

References

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LaFreniere, D., Zulkernine, F., Barber, D., and Martin, K. Using machine learning to predict hypertension from a clinical dataset. In 2016 IEEE symposium series on computational intelligence (SSCI). IEEE, pp. 1–7, 2016. DOI: https://doi.org/10.1109/SSCI.2016.7849886

Zarate, L., Petrocchi, B., Maia, C., Felix, C., and Gomes, M. P. Capto- a method for understanding problem domains for data science projects. Concilium 23:922–941, 2023. DOI: https://doi.org/10.53660/CLM-1815-23M33

Published

2024-11-19

How to Cite

de Carvalho, N. M., Gomes, M. P. S., & Zárate, L. E. (2024). Data mining in the diagnosis of hypertension based on National Health Survey 2019. Journal of Health Informatics, 16(Especial). https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1250

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