Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project

PLoS ONE

Volumn 12, Issue 7, 2017, Pages

  Alghamdi, Manal ^a   Al Mallah, Mouaz ^a,b   Keteyian, Steven ^b   Brawner, Clinton ^b   Ehrman, Jonathan ^b   Sakr, Sherif ^a  

^a KING SAUD BIN ABDULAZIZ UNIVERSITY FOR HEALTH SCIENCES   (Saudi Arabia)

^b HENRY FORD HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BAYESIAN LEARNING; CARDIORESPIRATORY FITNESS; CLASSIFIER; CONTROLLED STUDY; DECISION TREE; DEMOGRAPHY; DIABETES MELLITUS; ENSEMBLE; HUMAN; LOGISTIC MODEL TREE; LOGISTIC REGRESSION ANALYSIS; MACHINE LEARNING; MAJOR CLINICAL STUDY; MEASUREMENT ACCURACY; MEASUREMENT PRECISION; MEDICAL HISTORY; MULTIPLE LINEAR REGRESSION ANALYSIS; PREDICTION; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; SMOTE; TREADMILL EXERCISE; VITAL SIGN; BAYES THEOREM; COMPLICATION; CORONARY ARTERY DISEASE; EXERCISE TEST; FEMALE; FITNESS; HEART FAILURE; MALE; PATHOPHYSIOLOGY; PHYSIOLOGY; PROCEDURES; RISK FACTOR; STATISTICAL MODEL;

BAYES THEOREM; CORONARY ARTERY DISEASE; DECISION TREES; DIABETES MELLITUS; EXERCISE TEST; FEMALE; HEART FAILURE; HUMANS; LOGISTIC MODELS; MACHINE LEARNING; MALE; PHYSICAL FITNESS; RISK FACTORS;

EID: 85025430136     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0179805     Document Type: Article

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