Application of Probabilistic Techniques for the Development of a Prognosis Model of Stroke Using Epidemiological Studies

Abstract

Automated medical diagnosis systems based on knowledge-oriented descriptions have gained momentum with the emergence of recent artificial intelligence techniques. The objective of this paper is to propose a design of a probabilistic model for the prevention of stroke based on the most outstanding risk factors associated with this pathology. The authors gather probabilistic technologies to develop a new clinical support decision-making model. This development is part of a future system that aims to improve health-quality and prevent strokes. The Naïve Bayes model is proposed to calculate the probability of suffering a stroke in the future, based on epidemiological data. Due to a new design, the model is capable to determine the probability of suffering a stroke given some risk factors. The proposed model allows to calculate the final probability of suffering a specific disease for the preventive prognosis of the stroke based on risk factors. Our model enables query the probability of suffering a stroke giving as parameter the presence or absence of a specific indication, also setting if the indication can take several values with its presence, degree or value. With the obtained results the physician will be able to promote patients healthy living habits in order to prevent future stroke events.