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

Carlos Daniel González Cedillo

doi:10.18682/cyt.v19i19.1872

Carlos Daniel González Cedillo Universidad de Palermo

DOI: https://doi.org/10.18682/cyt.v19i19.1872

Palabras clave: diagnóstico de enfermedades, cardiacas, algoritmos

Resumen

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.

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