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CLC number: TP273; R544

On-line Access: 2015-06-04

Received: 2014-08-18

Revision Accepted: 2014-12-20

Crosschecked: 2015-05-07

Cited: 1

Clicked: 2578

Citations:  Bibtex RefMan EndNote GB/T7714


Gurmanik Kaur


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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.6 P.474-485


Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Author(s):  Gurmanik Kaur, Ajat Shatru Arora, Vijender Kumar Jain

Affiliation(s):  Sant Longowal Institute of Engineering and Technology, Deemed University, Punjab 148106, India

Corresponding email(s):   mannsliet@gmail.com, ajatsliet@yahoo.com, vkjain27@yahoo.com

Key Words:  Blood pressure (BP), Principal component analysis (PCA), Forward stepwise regression, Artificial neural network (ANN), Adaptive neuro-fuzzy inference system (ANFIS), Least squares support vector machine (LS-SVM)

Gurmanik Kaur, Ajat Shatru Arora, Vijender Kumar Jain. Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(6): 474-485.

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A1 - Gurmanik Kaur
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A1 - Vijender Kumar Jain
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DOI - 10.1631/FITEE.1400295

Accurate blood pressure (BP) measurement is essential in epidemiological studies, screening programmes, and research studies as well as in clinical practice for the early detection and prevention of high BP-related risks such as coronary heart disease, stroke, and kidney failure. Posture of the participant plays a vital role in accurate measurement of BP. Guidelines on measurement of BP contain recommendations on the position of the back of the participants by advising that they should sit with supported back to avoid spuriously high readings. In this work, principal component analysis (PCA) is fused with forward stepwise regression (SWR), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and the least squares support vector machine (LS-SVM) model for the prediction of BP reactivity to an unsupported back in normotensive and hypertensive participants. PCA is used to remove multi-collinearity among anthropometric predictor variables and to select a subset of components, termed ‘principal components’ (PCs), from the original dataset. The selected PCs are fed into the proposed models for modeling and testing. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that a PCA-based LS-SVM (PCA-LS-SVM) model is a promising approach for the prediction of BP reactivity in comparison to others. This assessment demonstrates the importance and advantages posed by hybrid models for the prediction of variables in biomedical research studies.

The paper deals with the evaluation of the uncertainty of BP measurement in the case of unsupported back. The paper is well written.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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