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Journal of Healthcare Engineering
Volume 2017 (2017), Article ID 2187904, 13 pages
Research Article

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

1Department of Electrical Engineering, SBBSU, Khiala, District Jalandhar, Punjab 144030, India
2Department of Electrical and Instrumentation Engineering, SLIET, Deemed University (Established by Govt. of India), Longowal, District Sangrur, Punjab 148106, India

Correspondence should be addressed to Gurmanik Kaur

Received 14 August 2017; Revised 5 October 2017; Accepted 16 October 2017; Published 26 November 2017

Academic Editor: Ming-Yuan Hsieh

Copyright © 2017 Gurmanik Kaur et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.