Diagnóstico de Enfermedades Card´ıacas con los algoritmos supervisados Naives Bayesian

• González Cedillo, Carlos Daniel ^[1]
  1. [1] Universidad de Palermo
• Localización: Ciencia y tecnología, ISSN 1850-0870, ISSN-e 2344-9217, Nº. 19, 2019, págs. 117-128
• Idioma: español
• DOI: 10.18682/cyt.v19i19.1872
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    Las enfermedades cardíacas son la principal causa de muerte en la actualidad. Este paper contrasta la performance de los diferentes algoritmos supervisados de Machine Learning, que tienen aplicaciones en el a´rea de la medicina, con los algoritmos supervisados Naives Bayes para ayudar a clasificar pacientes propensos a sufrir enfermedades cardíacas. Como fuente de datos se usan 303 instancias de pacientes con diferentes características que fueron analizados al procesar los datos con los respectivos algoritmos. Los resultados con el algoritmo de Naives Bayes son pro- metedores, obteniendo una precisio´n del 86,81 %, usando la fuente de datos mencionada. Esta familia de algoritmos tiene un mejor rendimiento comparado con otros algorit- mos de Machine Learning como neural networks, arrojando resultados ma´s precisas que las esperadas de me´dicos humanos.

  • English

    Heart disease is the leading cause of the death in the present. This paper contrasts the performance between the different supervised algorithms of Machine Learning, applied in medicine field, with the Naive Bayes supervised algorithms to help classify patients prone to heart disease. As data source, 303 instances of patients with different characteristics were used and analized when the data was proccessed by the respective algorithms. The results with the Naives Bayes algorithm are promising, obtaining an accuracy of 86.81 % using the mentioned data source. This family of algorithm has a better performance compared to other Machine Learning algorithms such as Neural Networks, obtaining more precise results than those expected from humans doctors.

• Referencias bibliográficas
  • G. F. Tomaselli and D. P. Zipes, “What causes sudden death in heart failure?” Circulation research, vol. 95, no. 8, pp. 754–763, 2004.
  • V. Autores, “Risk factors for heart diseases,” urlhttps://www.webmd.com/heart-disease/risk-factors-for-heart- disease.
  • R. Vijayakrishnan, S. R. Steinhubl, K. Ng, J. Sun, R. J. Byrd,
  • Z. Daar, B. A. Williams, C. Defilippi, S. Ebadollahi, and W. F. Stewart, “Prevalence of heart failure signs and symptoms in a large primary...
  • R. C. Deo, “Machine learning in medicine,” Circulation, vol. 132, no. 20, pp. 1920–1930, 2015.
  • J. Khan, J. S. Wei, M. Ringner, L. H. Saal, M. Ladanyi, F. Wes- termann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson et al., “Classification...
  • A. Rajkumar and G. S. Reena, “Diagnosis of heart disease using datamining algorithm,” Global journal of computer science and technology, vol....
  • I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.
  • J. R. Quinlan, “Induction of decision trees,” Machine learning, vol. 1, no. 1, pp. 81–106, 1986.
  • H. Zhang, “The optimality of naive bayes,” AA, vol. 1, no. 2, p. 3, 2004.
  • G. I. Webb and M. J. Pazzani, “Adjusted probability naive bayesian induction,” in Australian Joint Conference on Artificial Intelligence....
  • Q. Kuang and L. Zhao, “A practical gpu based knn algorithm,” in Proceedings. The 2009 International Symposium on Computer Science and Computational...
  • I. Kononenko, “Machine learning for medical diagnosis: history, state of the art and perspective,” Artificial Intelligence in medi- cine,...
  • M. Clinic, “Enfermedad cardiaca,” https://www.mayoclinic.org/es-es/diseases-conditions/heart- disease/symptoms-causes/syc-20353118.
  • S. U. Amin, K. Agarwal, and R. Beg, “Genetic neural network ba- sed data mining in prediction of heart disease using risk factors,” in 2013...
  • T. G. Dietterich, “Ensemble methods in machine learning,” in International workshop on multiple classifier systems. Springer, 2000, pp. 1–15.
  • D. W. Hosmer Jr, S. Lemeshow, and R. X. Sturdivant, Applied logistic regression. John Wiley & Sons, 2013, vol. 398.
  • I. Rish et al., “An empirical study of the naive bayes classifier,” in IJCAI 2001 workshop on empirical methods in artificial intelligence,...
  • M. J. Mizianty, L. A. Kurgan, and M. R. Ogiela, “Discretization as the enabling technique for the naive bayes and semi-naive bayes-based classification,”...
  • O. Harrison, “Machine learning basics with the k-nearest neighbors algorithm,” https://towardsdatascience.com/machine- learning-basics-with-the-k-nearest-neighbors-algorithm-...
  • C. Nicholson, “A beginner’s guide to neural networks and deep learning,” https://pathmind.com/wiki/neural-network.