Title :
A Bayesian model for disease prediction using symptomatic information
Author :
Pinango, A. ; Dorado, R.
Author_Institution :
Fac. de Ing., Univ. EAN, Bogota, Colombia
Abstract :
This paper addresses the problem of prediction of diseases based on specific symptoms in order to improve medical attention given to patients. We propose a flexible Bayesian framework for modeling symptom association with disease in population-based studies. We employ a Bayesian probabilistic model to describe the correlation between specific symptoms such as fever its cause. The effectiveness of the model is tested with a similarity based measure and training data.
Keywords :
Bayes methods; data mining; diseases; medical information systems; patient diagnosis; Bayesian probabilistic model; data mining; disease prediction; fever; flexible Bayesian framework; medical attention; medical information system; patient diagnosis; population-based studies; similarity based measure; symptom association modeling; symptomatic information; Bayes methods; Data mining; Diseases; Medical diagnostic imaging; Predictive models; Probabilistic logic; Unified modeling language; Bayes methods; data mining; medical information systems; patient diagnosis;
Conference_Titel :
Central America and Panama Convention (CONCAPAN XXXIV), 2014 IEEE
Conference_Location :
Panama City
DOI :
10.1109/CONCAPAN.2014.7000397
