Detecting knee osteoarthritis and its discriminating parameters using random forests

Medical Engineering and Physics

Volumn 43, Issue , 2017, Pages 19-29

  Kotti, Margarita ^a,b   Duffell, Lynsey D ^a,c   Faisal, Aldo A ^b,d   McGregor, Alison H ^a  

^a CHARING CROSS HOSPITAL   (United Kingdom)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

^d HAMMERSMITH HOSPITAL   (United Kingdom)

Author keywords

Ground reaction forces; Knee osteoarthritis; Machine learning; Random forests

Indexed keywords

BIOPHYSICS; LEARNING SYSTEMS;

ANTERIOR POSTERIORS; AUTOMATIC DETECTION; GROUND REACTION FORCES; HEALTHY SUBJECTS; INTERPRETABILITY; KNEE OSTEOARTHRITIS; RANDOM FORESTS; RULE-BASED APPROACH;

DECISION TREES;

ADULT; ARTICLE; CLINICAL ARTICLE; CLINICAL FEATURE; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SYSTEM; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; FEMALE; FORCE PLATE; GAIT; GROUND REACTION FORCE; HUMAN; KINETICS; KNEE OSTEOARTHRITIS; LOCOMOTION; MALE; MIDDLE AGED; MOTION ANALYSIS SYSTEM; MUSCULOSKELETAL SYSTEM PARAMETERS; PIEZOELECTRICITY; PUSH OFF TIME; RANDOM FOREST; SENSITIVITY AND SPECIFICITY; SENSOR; VALIDATION PROCESS; WALKING; CASE CONTROL STUDY; MACHINE LEARNING; PROCEDURES;

CASE-CONTROL STUDIES; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; MACHINE LEARNING; OSTEOARTHRITIS, KNEE;

EID: 85013674713     PISSN: 13504533     EISSN: 18734030     Source Type: Journal    
DOI: 10.1016/j.medengphy.2017.02.004     Document Type: Article

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