Title :
Predicting in-hospital-death and mortality percentage using logistic regression
Author :
Hamilton, S.L. ; Hamilton, J.R.
Author_Institution :
Univ. of Oklahoma, Oklahoma City, OK, USA
Abstract :
Logistic regression is an appropriate analysis technique for this CinC Challenge problem. Derived variables from provided patient data records are screened for significance by linear stepwise regression. Screened derived variables and corresponding patient outcome data serve respectively as the predictor and response variables for logistic regression analysis. Each of the two CinC Challenge events use separate logistic regression models, and include limited investigation of non-linear effects. Short descriptions of excursions from the logistic regression approach summarize the scope of the effort.
Keywords :
logistics; medical information systems; regression analysis; CinC challenge problem; in-hospital-death prediction; linear stepwise regression; logistic regression models; mortality percentage prediction; nonlinear effects; patient data records; screened derived variables; Equations; Logistics; MATLAB; Mathematical model; Regression analysis; Sociology;
Conference_Titel :
Computing in Cardiology (CinC), 2012
Conference_Location :
Krakow
Print_ISBN :
978-1-4673-2076-4