Current computer systems are accumulating huge amounts of information in several application domains. The outbreak of COVID-19 has increased rekindled interest in the use of data mining techniques for the analysis of factors that are related to the emergence of an epidemic. Data mining techniques are being used in the analysis and interpretation of information, which helps in the discovery of patterns, planning of isolation policies, and even predicting the speed of proliferation of contagion in a viral disease such as COVID-19. This research provides a comprehensive study of various data mining algorithms that are used in conjunction with epidemiological prediction models. The document considers that there is an opportunity to improve or develop tools that offer an accurate prognosis in the management of viral diseases through the use of data mining tools, based on a comparative study of 35 research papers.

Resumen. El área de la Inteligencia Artificial ha incursionando en el ámbito médico con el propósito de apoyar en el reconocimiento automático de imágenes del cerebro. Una de las enfermedades que ha ido en aumento en la última década es la Enfermedad Cerebro Vascular, cuyo conjunto de enfermedades, afectan los vasos sanguíneos cerebrales. Dados los efectos que puede causar este tipo de enfermedad, es importante caracterizar las fuentes que generan imágenes médicas y los procedimientos empleados para mejorarlas e interpretarlas. El documento describe los avances reportados en la literatura en relación a las fuentes que obtienen las imágenes y las etapas seguidas para su reconocimiento. El trabajo considera que existe la oportunidad de mejorar o desarrollar más algoritmos avanzados, que ofrezca un nivel más alto en la detección de enfermedades en imágenes del cerebro. Palabras clave: enfermedad cerebro vascular, reconocimiento de imágenes médicas, inteligencia artificial, técnicas de visión artificial. State of the Art and Elements of Automatic Image Recognition of the Brain Abstract. Artificial Intelligence area has penetrated the medical field with the purpose of supporting the automatic recognition of brain imaging. One of the diseases that has been increasing in the last decade are Brain Stroke, whose set of diseases, affect the cerebral blood vessels. Given that effects that can cause this type of disease, it is important to characterize the sources that generate medical images and the methods used to improve and interpret them. This paper describes the advances reported in the literature in relation to the sources that obtain the images and the stages followed for their recognition. Moreover considers that there is an opportunity to improve and development of advanced algorithms, which will offer a higher level in the detection of diseases in brain images.

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