NDLI: Random forest: A reliable tool for patient response prediction

Content Provider	IEEE Xplore Digital Library
Author	Dittman, D. Khoshgoftaar, T.M. Wald, R. Napolitano, A.
Copyright Year	2011
Description	Author affiliation: Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida 33431 (Dittman, D.; Khoshgoftaar, T.M.; Wald, R.; Napolitano, A.)
Abstract	The goal of classification is to reliably identify instances that are members of the class of interest. This is especially important for predicting patient response to drugs. However, with high dimensional datasets, classification is both complicated and enhanced by the feature selection process. When designing a classification experiment there are a number of decisions which need to be made in order to maximize performance. These decisions are especially difficult for researchers in fields where data mining is not the focus, such as patient response prediction. It would be easier for such researchers to make these decisions if either their outcomes were chosen or their scope reduced, by using a learner which minimizes the impact of these decisions. We propose that Random Forest, a popular ensemble learner, can serve this role. We performed an experiment involving nineteen different feature selection rankers (eleven of which were proposed and implemented by our research team) to thoroughly test both the Random Forest learner and five other learners. Our research shows that, as long as a large enough number of features are used, the results of using Random Forest are favorable regardless of the choice of feature selection strategy, showing that Random Forest is a suitable choice for patient response prediction researchers who want to do not wish to choose from amongst a myriad of feature selection approaches.
Starting Page	289
Ending Page	296
File Size	874961
Page Count	8
File Format	PDF
ISBN	9781457716126
e-ISBN	9781457716133
DOI	10.1109/BIBMW.2011.6112389
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Measurement Drugs Accuracy patient response Buildings Vegetation Microarray Cancer Random Forest
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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