title: Machine learning approach to extract diagnostic and prognostic thresholds: application in prognosis of cardiovascular mortality.
abstract: Machine learning has become a powerful tool for analysing medical domains, assessing the importance of clinical parameters, and extracting medical knowledge for outcomes research. In this paper, we present a machine learning method for extracting diagnostic and prognostic thresholds, based on a symbolic classification algorithm called REMED. We evaluated the performance of our method by determining new prognostic thresholds for well-known and potential cardiovascular risk factors that are used to support medical decisions in the prognosis of fatal cardiovascular diseases. Our approach predicted 36% of cardiovascular deaths with 80% specificity and 75% general accuracy. The new method provides an innovative approach that might be useful to support decisions about medical diagnoses and prognoses.
authors.affiliation: Department of Computer Engineering, Polytechnic University of Sinaloa, 82199 Mazatlan, SIN, Mexico. lmena@upsin.edu.mx
authors.forename: Luis J
authors.initials: LJ
authors.lastname: Mena
authors.forename: Eber E
authors.initials: EE
authors.lastname: Orozco
authors.forename: Vanessa G
authors.initials: VG
authors.lastname: Felix
authors.forename: Rodolfo
authors.initials: R
authors.lastname: Ostos
authors.forename: Jesus
authors.initials: J
authors.lastname: Melgarejo
authors.forename: Gladys E
authors.initials: GE
authors.lastname: Maestre
mesh_terms: D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D018660:Blood Pressure Monitoring, Ambulatory / Q000295:instrumentation / Q000379:methods; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000473:pathology; D002319:Cardiovascular System; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012984:Software