Comparison of machine learning techniques with classical statistical models in predicting health outcomes

Studies in Health Technology and Informatics

Volumn 107, Issue , 2004, Pages 736-740

  Song, Xiaowei ^a   Mitnitski, Arnold ^b   Cox, Jafna ^b   Rockwood, Kenneth ^a,b  

^a QUEEN ELIZABETH II HEALTH SCIENCES CENTRE   (Canada)

^b DALHOUSIE UNIVERSITY   (Canada)

Author keywords

Classification; Health status; Machine learning; ROC curves

Indexed keywords

CLASSIFICATION (OF INFORMATION); FORECASTING; HEALTH; LEARNING ALGORITHMS; NETWORK LAYERS; NEURAL NETWORKS; POPULATION STATISTICS; STATISTICAL TESTS; SURVEYS; MEDICAL INFORMATICS; SUPPORT VECTOR MACHINES;

ACTIVITIES OF DAILY LIVING; HEALTH STATUS; MACHINE LEARNING TECHNIQUES; MULTI LAYER PERCEPTRON; NON-LINEAR RELATIONSHIPS; PHYSICAL EXAMINATIONS; ROC CURVES; SINGLE LAYER PERCEPTRON; HEALTH OUTCOMES; LEAST SQUARE; LINEAR SEPARATION; LOGISTICS REGRESSIONS; MACHINE-LEARNING; SINGLE-LAYER PERCEPTRONS; STATISTIC MODELING;

LEARNING SYSTEMS; MULTILAYER NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; CONFERENCE PAPER; CONTROLLED STUDY; HEALTH STATUS; HUMAN; HUMAN EXPERIMENT; HUMAN TISSUE; INTERMETHOD COMPARISON; LEARNING; MACHINE LEARNING; MULTILAYER PERCEPTRON; OUTCOME ASSESSMENT; SEPARATION TECHNIQUE;

EID: 77952577151     PISSN: 09269630     EISSN: 18798365     Source Type: Book Series    
DOI: 10.3233/978-1-60750-949-3-736     Document Type: Article

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