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

Abstract

Crossing the legs at the knees, during BP measurement, is one of the several physiological stimuli that considerably influence the accuracy of BP measurements. Therefore, it is paramount to develop an appropriate prediction model for interpreting influence of crossed legs on BP. This research work described the use of principal component analysis- (PCA-) fused forward stepwise regression (FSWR), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), and least squares support vector machine (LS-SVM) models for prediction of BP reactivity to crossed legs among the normotensive and hypertensive participants. The evaluation of the performance of the proposed prediction models using appropriate statistical indices showed that the PCA-based LS-SVM (PCA-LS-SVM) model has the highest prediction accuracy with coefficient of determination (R2) = 93.16%, root mean square error (RMSE) = 0.27, and mean absolute percentage error (MAPE) = 5.71 for SBP prediction in normotensive subjects. Furthermore, R2 = 96.46%, RMSE = 0.19, and MAPE = 1.76 for SBP prediction and R2 = 95.44%, RMSE = 0.21, and MAPE = 2.78 for DBP prediction in hypertensive subjects using the PCA-LSSVM model. This assessment presents the importance and advantages posed by hybrid computing models for the prediction of variables in biomedical research studies.

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“…Microsoft Excel 2013 was used to estimate and evaluate the parameters by fitting the experimental values to the proposed. The coefficient of determination ( R 2 ), chi‐square ( χ 2 ), root‐mean‐square error (RMSE), mean absolute percentage error (MAPE), and residual sum of squares (RSS) of each mathematical model were calculated, and a suitable model was chosen based on the goodness of fit with the highest value of R 2 and lowest value of χ 2 , RMSE, MAPE, and RSS (Afolabi, Tunde‐Akintunde, & Adeyanju, 2015; Kaur, Arora, & Jain, 2017).…”

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“…The solver add-in in Microsoft Excel 2013 was used to estimate and evaluate the parameters by fitting the experimental values to the proposed. The coefficient of determination (R 2 ), chi-square (χ 2 ), root mean square error (RMSE), mean absolute percentage error (MAPE) and residual sum of squares (RSS) of each mathematical model were calculated and a suitable model was chosen based on the goodness of fit with the highest value of R 2 and lowest value of χ 2 , RMSE, MAPE, and RSS (Afolabi et al, 2015;Kaur et al, 2017).…”