Combining the performance strengths of the logistic regression and neural network models: a medical outcomes approach.
The assessment of medical outcomes is important in the effort to contain costs, streamline patient management, and codify medical practices. As such, it is necessary to develop predictive models that will make accurate predictions of these outcomes. The neural network methodology has often been shown to perform as well, if not better, than the logistic regression methodology in terms of sample predictive performance. However, the logistic regression method is capable of providing an explanation regarding the relationship(s) between variables. This explanation is often crucial to understanding the clinical underpinnings of the disease process. Given the respective strengths of the methodologies in question, the combined use of a statistical (i.e., logistic regression) and machine learning (i.e., neural network) technology in the classification of medical outcomes is warranted under appropriate conditions. The study discusses these conditions and describes an approach for combining the strengths of the models.
Wong
Wun
W
McKesson Health Solutions, 275 Grove Street, Suite 1-110, Newton, MA 02466, USA. wun.wong@McKesson.com
Fos
Peter J
PJ
Petry
Frederick E
FE
D001185:Artificial Intelligence; D006333:Heart Failure / Q000175:diagnosis / Q000191:economics / Q000401:mortality / Q000628:therapy; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017063:Outcome Assessment, Health Care / Q000191:economics / Q000379:methods* / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D012189:Retrospective Studies