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Comparative study of artificial intelligence techniques for the diagnosis of diseases in livestock

Abstract:

The diagnosis of diseases is a complex cognitive process that involves training, experience, recognition of patterns and calculation of conditional probability, among other components less understood. In recent decades, several efforts have been made to apply pbkp_redictive analysis in health systems, as well as to launch automatic learning systems to facilitate the diagnosis of diseases. Currently, medicine (animal and human) uses considerable advances that involve the intensive use of high technology such as diagnostic imaging, robotics, among others, especially the application of Artificial Intelligence (AI) techniques, which are feasible when they take advantage of the available data and clinical experience. The objective of this work is to compare AI techniques to select the one that best fits the diagnosis of diseases in livestock, when stored data on the behavior of diseases that often affect the livestock.