Abstract
ABSTRACTEstimation of person-specific risk for adverse health events in medicine has been approached almost exclusively using parametric statistical methods. Random forest is a machine learning method based on tree ensembles that is completely nonparametric and for this reason may be better suited for risk prediction. An introduction to a random forest is provided with a focus on its application to risk prediction. Using data from a total joint replacement registry, we illustrate risk prediction for the binary outcome of 90-day mortality following implantation, as well as time to device failure for aseptic reasons with the competing risk of mortality. Using the methods described in this paper, the random forest could be applied to risk prediction in a wide variety of medical fields. Issues related to implementation are discussed.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
