Figure 1.
Artificial neural network model showing input variables (nodes), hidden nodes, and connection weights with output node for data on CA dysfunction.
The ANN model including 14 input nodes, 18 hidden nodes and 1 output node. Data from a total of 2077 patients had been used to ANN analysis. BMI- Body mass index, WC-waist circumference, SBP- systolic blood pressure, DBP- diastolic blood pressure, FPG- fasting plasma glucose, PBG- plasma blood glucose, IR-insulin resistance, TG- triglyceride, UA- uric acid, HR-heart rate, PH- Hypertension, DM- Diabetes, PHD- Hypertension duration, DMD- Diabetes duration.
Table 1.
Baseline characteristics of subject.
Table 2.
Univariate analysis for CA dysfunction.
Table 3.
Final models using Multivariate logistic linear analysis for CA dysfunction.
Table 4.
Prediction models using multiple logistic regression and artificial neural network.
Table 5.
Comparisons between models from Multiple logistic regression and Artificial neural network analysis.