Prediction of Incident Diabetes in the Jackson Heart Study Using High-Dimensional Machine Learning

Statistical models to predict incident diabetes are often based on limited variables. Here we pursued two main goals: 1) investigate the relative performance of a machine learning method such as Random Forests (RF) for detecting incident diabetes in a high-dimensional setting defined by a large set...

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Veröffentlicht in:	PloS one 2016-10, Vol.11 (10), p.e0163942-e0163942
Hauptverfasser:	Casanova, Ramon, Saldana, Santiago, Simpson, Sean L, Lacy, Mary E, Subauste, Angela R, Blackshear, Chad, Wagenknecht, Lynne, Bertoni, Alain G
Format:	Artikel
Sprache:	eng
Schlagworte:	Adiponectin Adiponectin - blood Adult African Americans Aged Aldosterone Anthropometry Biology and Life Sciences Biomarkers Biomarkers - blood Blood Glucose - analysis C-reactive protein C-Reactive Protein - analysis Cholesterol Cholesterol, HDL - blood Chronic illnesses Computer and Information Sciences Demographic variables Demographics Demography Development and progression Diabetes mellitus Diabetes Mellitus, Type 2 - epidemiology Echocardiography Female Follow-Up Studies Glucose Glycated Hemoglobin A - analysis Glycosylated hemoglobin Health risk assessment Heart Hemoglobin Humans Incidence Insulin resistance Learning algorithms Leptin Leptin - blood Machine Learning Male Mathematical models Medicine and Health Sciences Middle Aged Minority & ethnic groups Models, Theoretical People and places Physical Sciences Regression analysis Research and Analysis Methods Statistical analysis Statistical models Studies Systematic review Teaching methods Triglycerides Triglycerides - blood Type 2 diabetes Ventricle Waist Circumference
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