. 2021 Jun 25;11(3):273–286. doi: 10.1007/s13534-021-00196-7

Table 9.

Comparison with existing studies of diabetes prediction using non-linear HRV features

References	Sample size	Non-linear HRV features	Number of features	Methods	AC (%)	SE (%)	SP (%)	Model validation on the number of patients
Acharya et al. [35]	Control-15 Diabetes-15	Poincare features, Correlation dimension, recurrence plot features	5	Adaboost with Least Squares	86	87.50	84.60	–
Acharya et al. [36]	Control-15 Diabetes-15	AppEN, Recurrence plot, Lyapunov exponent, DFA	9	Perceptron Adaboost	90	92.52	88.72	–
In this work	Control-35 Diabetes-100	SD1 + SD2	2	GPC-Linear kernel	92.59	96.07	81.81	Total-65 (Control-15, Diabetes-50)

References

Sample size

Non-linear HRV features

Number of features

Methods

AC (%)

SE (%)

SP (%)

Model validation on the number of patients

Acharya et al. [35]

Control-15

Diabetes-15

Poincare features, Correlation dimension, recurrence plot features

Adaboost with Least Squares

87.50

84.60

–

Acharya et al. [36]

Control-15

Diabetes-15

AppEN, Recurrence plot, Lyapunov exponent, DFA

Perceptron Adaboost

92.52

88.72

–

In this work

Control-35

Diabetes-100

SD1 + SD2

GPC-Linear kernel

92.59

96.07

81.81

Total-65

(Control-15,

Diabetes-50)