Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes – ELSA-Brasil: Accuracy study;Comparação de algoritmos de aprendizagem de máquina para construir um modelo preditivo para detecção de diabetes não diagnosticada – ELSA-Brasil: Estudo de acurácia

Sao Paulo Medical Journal

Volumn 135, Issue 3, 2017, Pages 234-246

  Olivera, André Rodrigues ^a   Roesler, Valter ^a   Iochpe, Cirano ^a   Schmidt, Maria Inês ^a   Vigo, Álvaro ^a   Barreto, Sandhi Maria ^b   Duncan, Bruce Bartholow ^a  

^a FEDERAL UNIVERSITY OF RIO GRANDE DO SUL   (Brazil)

^b FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

Data mining; Decision support techniques; Diabetes mellitus, type 2; Models, statistical; Supervised machine learning

Indexed keywords

GLUCOSE; HEMOGLOBIN A1C;

ADULT; AGED; ALGORITHM; ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYESIAN LEARNING; BOOTSTRAPPING; CARDIOVASCULAR RISK; COMPUTER PREDICTION; CONTROLLED STUDY; DIABETES MELLITUS; FEMALE; HEALTH CARE PERSONNEL; HUMAN; HYPERGLYCEMIA; K NEAREST NEIGHBOR; LONGITUDINAL STUDY; MACHINE LEARNING; MALE; MIDDLE AGED; MULTICENTER STUDY; NORMAL HUMAN; OBESITY; PERCEPTRON; PHYSICAL ACTIVITY; RANDOM FOREST; RISK FACTOR; TRAINING; VALIDATION PROCESS; BAYES THEOREM; BRAZIL; COMPARATIVE STUDY; COMPUTER SIMULATION; DIABETES MELLITUS, TYPE 2; FEASIBILITY STUDY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STANDARDS; STATISTICAL MODEL; SUPERVISED MACHINE LEARNING; VALIDATION STUDY;

ADULT; AGED; ALGORITHMS; BAYES THEOREM; BRAZIL; COMPUTER SIMULATION; DIABETES MELLITUS, TYPE 2; FEASIBILITY STUDIES; FEMALE; HUMANS; LOGISTIC MODELS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUPERVISED MACHINE LEARNING;

EID: 85026398611     PISSN: 15163180     EISSN: None     Source Type: Journal    
DOI: 10.1590/1516-3180.2016.0309010217     Document Type: Article

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