Saltar al contenido
República del Perú · Observatorio de la producción científica universitaria
OApol.org Observatorio de Inteligencia Científica Universitaria del Perú
OApol.org
Inicio Universidades Publicaciones Investigadores
Repositorios
Tesis y trabajos académicosRevistas
Indicadores
Ranking trimestralRanking por carreraObjetivos de Desarrollo SostenibleReporte OCDERed neuronal 3D
Acerca
MetodologíaPrivacidad

Self-Management to Anticipate Stroke in Diabetic Patients Through Algorithm Based on Probability of Bayes

Nieto-Chaupis H. · International Conference on Electrical, Computer and Energy Technologies, ICECET 2023 (2023) · DOI: 10.1109/icecet58911.2023.10389192

Resumen

This paper presents a scheme of self-management that employs directly the theorem of Bayes to calculate realistic probabilities to experience stroke in the shortest and middle terms. In concrete the probabilities might be used in an application by which diabetic patients can carry out by themselves periodical measurements of probabilities of risk. It is emphazised the fact that Bayes rule can be powerful but used as tool to predict stroke might also to trigger false alarms or be blind to stroke.

Instituciones: Universidad Autónoma del Perú