Using blood indexes to predict overweight statuses: An extreme learning machine-based approach

PLoS ONE

Volumn 10, Issue 11, 2015, Pages

  Chen, Huiling ^a,b   Yang, Bo ^b   Liu, Dayou ^b   Liu, Wenbin ^a   Liu, Yanlong ^c   Zhang, Xiuhua ^d   Hu, Lufeng ^d  

^a WENZHOU UNIVERSITY   (China)

^b JILIN UNIVERSITY   (China)

^c WENZHOU MEDICAL UNIVERSITY   (China)

^d THE FIRST AFFILIATED HOSPITAL OF WENZHOU MEDICAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALANINE AMINOTRANSFERASE; CREATININE; GAMMA GLUTAMYLTRANSFERASE; HIGH DENSITY LIPOPROTEIN; URIC ACID;

ADULT; AGED; ALANINE AMINOTRANSFERASE BLOOD LEVEL; ARTICLE; BIOCHEMICAL INDEX; BIOCHEMISTRY; BLOOD CHEMISTRY; BLOOD INDEX; BODY MASS; CONTROLLED STUDY; CORRELATION ANALYSIS; CREATININE BLOOD LEVEL; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; ERYTHROCYTE; FEMALE; GAMMA GLUTAMYL TRANSFERASE BLOOD LEVEL; HEMATOCRIT; HEMATOLOGICAL PARAMETERS; HUMAN; LIPID BLOOD LEVEL; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; OBESITY; PREDICTION; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; URIC ACID BLOOD LEVEL; AREA UNDER THE CURVE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; BLOOD; CASE CONTROL STUDY; REPRODUCIBILITY; SUPPORT VECTOR MACHINE;

AREA UNDER CURVE; CASE-CONTROL STUDIES; FEMALE; HUMANS; MACHINE LEARNING; MALE; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); OVERWEIGHT; REPRODUCIBILITY OF RESULTS; SUPPORT VECTOR MACHINE;

EID: 84958012547     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0143003     Document Type: Article

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