Using methods from the data-mining and machine-learning literature for disease classification and prediction: A case study examining classification of heart failure subtypes

Journal of Clinical Epidemiology

Volumn 66, Issue 4, 2013, Pages 398-407

  Austin, Peter C ^a,b,c   Tu, Jack V ^a,b,d   Ho, Jennifer E ^e,f,g   Levy, Daniel ^e,f,g   Lee, Douglas S ^a,b,d  

^a INSTITUTE FOR CLINICAL EVALUATIVE SCIENCES   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

^c UNIVERSITY OF TORONTO   (Canada)

^d UNIVERSITY OF TORONTO   (Canada)

^e FRAMINGHAM HEART STUDY   (United States)

^f NATIONAL HEART LUNG AND BLOOD INSTITUTE   (United States)

^g BOSTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

Bagging; Boosting; Classification; Classification trees; Heart failure; Prediction; Random forests; Regression methods; Regression trees; Support vector machines

Indexed keywords

AGED; ARTICLE; BOOSTING; BOOTSTRAP AGGREGATION; CANADA; CARDIAC PATIENT; CASE STUDY; DATA MINING; DISEASE CLASSIFICATION; HEART EJECTION FRACTION; HEART FAILURE; HUMAN; INFORMATION PROCESSING; LOGISTIC REGRESSION ANALYSIS; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MEDICAL LITERATURE; POPULATION RESEARCH; PRIORITY JOURNAL; PROBABILITY; PROGNOSIS; RANDOM FOREST; SUPPORT VECTOR MACHINE;

ARTIFICIAL INTELLIGENCE; DATA MINING; HEART FAILURE; HUMANS; PREDICTIVE VALUE OF TESTS; REGRESSION ANALYSIS;

EID: 84875243337     PISSN: 08954356     EISSN: 18785921     Source Type: Journal    
DOI: 10.1016/j.jclinepi.2012.11.008     Document Type: Article

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