Comparative Analysis of Hybrid Models for Prediction of BP Reactivity to Crossed Legs

Table 5

Comparison of results with other studies.

Ref.	Model developed	Predicted parameter	Results
[26]	Ridge linear regression, ANN, SVM, and random forest	BGL, BP	Random forest technique outperformed ridge linear regression, ANN, and SVM. R^2 = 0.91% (SBP), R^2 = 0.89% (DBP), and R^2 = 0.90% (BGL)
[28]	ANN (raw input), ANN (feature based), MAA, and ANFIS (feature based)	SBP, DBP	ANN (feature based) achieved the best performance compared to other models. For SBP predictions: MAE = 6.28, SDE = 8.58. For DBP predictions: MAE = 5.73, SDE = 7.33
[29]	ANN	SBP, DBP	The experimental results confirmed the correctness of the ANN when compared with the linear regression model. Mean ± σ: SBP: 3.80 ± 3.46, DBP: 2.21 ± 2.09. Relative error: SBP: 3.48 ± 3.19. DBP: 3.90 ± 3.51
[32]	SVM with RBF and polynomial kernel	SBP, DBP	SVM (RBF kernel) outperformed SVM (polynomial kernel). Coefficient of correlation (R) = 0.97 (SBP), 0.96 (DBP). RMSE = 6.94 (SBP), and 5.78 (DBP). Scatter index (SI) = 22.34 (SBP), 22.79 (DBP)
[36]	PCA-ANN, PCA-ANFIS, and PCA-LS-SVM	SBP, DBP	PCA-LS-SVM outperformed PCA-ANN and PCA-ANFIS.; For normotensive subjects: SBP: R^2 = 95.42%, RMSE = 0.21, and MAPE = 5.88%. DBP: R^2 = 94.22%, RMSE = 0.24, and MAPE = 4.05%. For hypertensive subjects: SBP: R^2 = 98.76%, RMSE = 0.11, and MAPE = 0.88%. DBP: R^2 = 98.78%, RMSE = 0.11, and MAPE = 0.84%
[37]	PCA-SWR, PCA-ANN, PCA-ANFIS, and PCA-LS-SVM	DBP	PCA-LS-SVM outperformed PCA-FSWR, PCA-ANN, and PCA-ANFIS. For normotensive subjects: R^2 = 98.49%, RMSE = 0.1243, and MAPE = 3.01%. For hypertensive subjects: R^2 = 95.95%, RMSE = 0.2013, and MAPE = 2.9%
[58]	ANN, ANFIS, and SVM	River flow in the semiarid mountain region	In comparing the results of the ANN, ANFIS, and SVM models, it was seen that the values of R, RMSE, mean absolute relative error (MARE), and Nash-Sutcliffe (NS) of the SVM model were higher than those of ANN and ANFIS for all combinations of input data
[59]	ANN, ANFIS	To predict depths-to-water table one month in advance, at three wells located at different distances from the river	Both models can be used with a high level of precision to the model water tables without a significant effect of the distance of the well from the river, as model precision expressed via RMSE was roughly the same in all three cases (0.14154–0.15248). R varied from 0.91973 to 0.9623 and coefficient of efficiency (COE) from 0.84588 to 0.92586
[60]	ANN, ANFIS, and SVM	Longitudinal dispersion coefficient (LDC)	The SVM model was found to be superior (R^2 = 90%) in predicting LDC due to low uncertainty as compared with those in the ANN (R^2 = 82%) and ANFIS (R^2 = 83%) models, while the ANFIS model performed better than the ANN model
[61]	Multilayer perceptron (MLP), ANN, fuzzy genetic (FG), LS-SVM, multivariate adaptive regression spline (MARS), ANFIS, multiple linear regression (MLR), and Stephens and Stewart models (SS)	Evaporation in different climates	The accuracies of the applied models were rank as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS, and MLR
Present study	PCA-FSWR, PCA-ANN, PCA-ANFIS, and PCA-LS-SVM	BP reactivity to crossed legs	PCA-LS-SVM outperformed PCA-FSWR, PCA-ANN, and PCA-ANFIS. For normotensive subjects: SBP: R^2 = 93.16%, RMSE = 0.27, and MAPE = 5.71%. For hypertensive subjects: SBP: R^2 = 96.46%, RMSE = 0.19, and MAPE = 1.76%. DBP: R^2 = 95.44%, RMSE = 0.21, and MAPE = 2.78%