APH 2010, 68, 143-154:
Using neural network as a screening and educational tool for abnormal glucose tolerance in the community
Gao W, Dong F, Nie S, Shi L
Keywords: screening, diabetes mellitus, Abnormal Glucose Tolerance, risk factors, neural networks
Accurate, simple and non-invasive tools are needed for efficient screening of abnormal glucose
tolerance (AGT) and educating the general public.
To develop a neural network-based initial screening and educational model for AGT.
Data and methods
230 subjects with AGT and 3,243 subjects with normal glucose tolerance (NGT) were allocated
into training, validation and test sets using stratified randomization. The ratios of AGT
versus NGT in three groups were 150:50, 30:570 and 50:950, respectively. A feed-forward
neural network (FFNN) was trained to predict 2-hour plasma glucose of 75g Oral Glucose
Tolerance Test (OGTT) using age, family history of diabetes, weight, height, waist and hip
circumference. The screening performance was evaluated by the area under the receiver
operating characteristic (ROC) curve (AUC) and the partial AUC (in the range of false positive
rates between 35 and 65%) and compared to those from logistic regression, linear
regression and ADA Risk Test.
Sensitivity, specificity, accuracy and percentage that needed further testing at 7.2mmol/L in
test group were 90.0 %( 95%CI: 78.6 to 95.7%), 47.7% (95%CI: 44.5 to 50.9%), 49.8%
(95%CI: 46.7 to 52.9%) and 54.2% (95%CI: 51.1 to 57.3%) respectively. The entire and partial
AUCs were 0.70 (95%CI: 0.62 to 0.78) and 0.26 (95%CI: 0.22 to 0.30). The partial AUC
of the NN was higher than those of logistic regression (p=0.06), linear regression (p=0.06)
and ADA Risk Test (P=0.006).
NN can be used as a high-sensitive and non-invasive initial screening and educational tool