Discriminating diabetes mellitus from single-lead electrocardiography using machine learning and multinomial regression

Table 1 Adjusted pairwise comparisons for multiple comparisons using the Holm method

Factor	No DM: T1DM	No DM: T2DM	T1DM - T2DM
HFNoise	0.660	0.009	0.660
Ventricular activation	0.704	0.009	0.210
Prolonged QT interval	0.000	0.024	0.034
QT_TQ	0.001	0.284	0.022
HFQRS	0.000	0.000	0.000
HFSNR	0.000	0.000	0.000
T-wave amplitude	0.078	0.078	0.697
QRS12energy	0.024	0.541	0.108
QRSE2	0.033	0.639	0.084
QRSw	0.084	0.006	0.878
Pan	0.078	0.128	0.457
RA	0.129	0.129	0.519
SA	0.024	0.484	0.136
Pst	0.000	0.000	0.007
P-wave dispersion index	0.000	0.001	0.001
QRSst	0.000	0.000	0.001
QRSfi	0.000	0.000	0.000
T-wave flattening	0.000	0.000	0.637
PpeakN	0.000	0.000	0.000
Rpeak	0.000	0.721	0.001
Speak	0.001	0.546	0.016
Tpeak	0.158	0.158	0.048
Toffs	0.192	0.192	0.051
RonsF	0.026	0.026	0.002
RoffsF	0.004	0.188	0.002
SDNN	0.018	0.278	0.222

DM: Diabetes mellitus; T1DM: Type 1 diabetes mellitus; T2DM: Type 1 diabetes mellitus.

Table 2 The primary predictors with their coefficient estimation, 95%CI, subsequent exponentiation, and odds ratio

	Type 1 DM			Type 2 DM
	OR	95%CI	P value	OR	95%CI	P value
Intercept	0.016	0.005-0.048	< 0.001	1.391	1.049-1.845	0.022
Smoking	0.156	0.110-0.221	< 0.001	0.662	0.100-4.401	0.670
Prolonged QT interval	3.810	1.222-11.875	0.021	0.351	0.197-0.624	< 0.001
T-wave amplitude	4.033	1.819-8.941	0.001	11.218	3.778-33.312	< 0.001
Pan	1.537	0.917-2.577	0.103	4.738	2.943-7.630	< 0.001
RA	1.185	0.846-1.659	0.324	2.668	1.505-4.730	0.001
SA	0.947	0.556-1.611	0.840	1.286	0.940-1.757	0.115
Pst	0.714	0.533-0.957	0.024	0.682	0.442-1.053	0.085
T-wave flattening	0.605	0.350-1.046	0.072	0.972	0.716-1.320	0.854
RoffsF	0.954	0.710-1.282	0.755	0.544	0.133-2.223	0.397
Age	0.954	0.607-1.500	0.839	0.980	0.481-1.997	0.956
P-wave dispersion index	0.725	0.540-0.974	0.033	0.732	0.169-3.177	0.677
PpeakN	1.510	0.965-2.361	0.071	1.946	0.865-4.376	0.108

DM: Diabetes mellitus; OR: Odds ratio.

Table 3 The built machine learning model performance in the differentiation of control, type 1 diabetes mellitus, and type 2 diabetes mellitus, mean (95%CI)

Statistic	No DM	Type 1 DM	Type 2 DM
Area under the curve	0.82 (0.76-0.87)	0.84 (0.76-0.91)	0.69 (0.61-0.76)
Sensitivity	0.65 (0.57-0.72)	0.85 (0.68-1.00)	0.91 (0.81-0.98)
Specificity	0.89 (0.81-0.97)	0.69 (0.63-0.75)	0.42 (0.34-0.49)
Positive predictive value	0.94 (0.89-0.98)	0.21 (0.13-0.32)	0.28 (0.21-0.36)
Negative predictive value	0.51 (0.42-0.60)	0.98 (0.95-1.00)	0.95 (0.89-0.99)

DM: Diabetes mellitus.

Table 4 The one-versus-one classification-built model performance

Groups comparing	Area under the curve	Lower limit 95%CI	Upper limit 95%CI
No DM against T1DM	0.8981013	0.8439558	0.9522468
No DM against T2DM	0.7758087	0.7054986	0.8461189
T1DM against T2DM	0.6366667	0.4914294	0.7819039

DM: Diabetes mellitus; T1DM: Type 1 diabetes mellitus; T2DM: Type 2 diabetes mellitus.

Table 5 The coefficient estimates of selected factors from the final model, along with their 95%CI

Parameter	Estimated coefficient	Lower limit 95%CI	Upper limit 95%CI	Р value
Intercept	5.852	4.950	6.753	0.0000000
HFSNR	-0.058	-0.159	0.043	0.261
QRSfi	-0.001	-0.003	0.0009	0.253
T-wave flattening	0.0018	0.001	0.003	0.0000114
PpeakN	-0.0017	-0.004	0.0009	0.202

A weak to moderate correlation was observed between the true and predicted values (Spearman’s rho = 0.488, P < 0.001).