Prediction of Incident Diabetes in the Jackson Heart Study Using High-Dimensional Machine Learning.
Journal:
PloS one
PMID:
27727289
Abstract
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 of observational data, and 2) uncover potential predictors of diabetes. The Jackson Heart Study collected data at baseline and in two follow-up visits from 5,301 African Americans. We excluded those with baseline diabetes and no follow-up, leaving 3,633 individuals for analyses. Over a mean 8-year follow-up, 584 participants developed diabetes. The full RF model evaluated 93 variables including demographic, anthropometric, blood biomarker, medical history, and echocardiogram data. We also used RF metrics of variable importance to rank variables according to their contribution to diabetes prediction. We implemented other models based on logistic regression and RF where features were preselected. The RF full model performance was similar (AUC = 0.82) to those more parsimonious models. The top-ranked variables according to RF included hemoglobin A1C, fasting plasma glucose, waist circumference, adiponectin, c-reactive protein, triglycerides, leptin, left ventricular mass, high-density lipoprotein cholesterol, and aldosterone. This work shows the potential of RF for incident diabetes prediction while dealing with high-dimensional data.
Authors
Keywords
Adiponectin
Adult
Aged
Anthropometry
Biomarkers
Black or African American
Blood Glucose
C-Reactive Protein
Cholesterol, HDL
Demography
Diabetes Mellitus, Type 2
Female
Follow-Up Studies
Glycated Hemoglobin
Humans
Incidence
Leptin
Machine Learning
Male
Middle Aged
Models, Theoretical
Triglycerides
Waist Circumference