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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 762501, 13 pages
Research Article

Prediction of BP Reactivity to Talking Using Hybrid Soft Computing Approaches

Electrical and Instrumentation Engineering Department, Sant Longowal Institute of Engineering & Technology, Deemed University (Established by Government of India), Longowal, Sangrur District, Punjab 148106, India

Received 3 June 2014; Revised 25 August 2014; Accepted 29 August 2014; Published 21 September 2014

Academic Editor: Ezequiel López-Rubio

Copyright © 2014 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.


High blood pressure (BP) is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI), and arm circumference (AC) were used as independent predictor variables for the prediction of BP reactivity to talking. Principal component analysis (PCA) was fused with artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), and least square-support vector machine (LS-SVM) model to remove the multicollinearity effect among anthropometric predictor variables. The statistical tests in terms of coefficient of determination (), root mean square error (RMSE), and mean absolute percentage error (MAPE) revealed that PCA based LS-SVM (PCA-LS-SVM) model produced a more efficient prediction of BP reactivity as compared to other models. This assessment presents the importance and advantages posed by PCA fused prediction models for prediction of biological variables.