Key result
A PCA-based LS-SVM model was a promising approach for predicting blood pressure reactivity to an unsupported back compared to other hybrid models.
Why the study?
Does a PCA-based LS-SVM model accurately predict blood pressure reactivity to an unsupported back based on anthropometric characteristics in normotensive and hypertensive participants?
Does a PCA-based LS-SVM model accurately predict blood pressure reactivity to an unsupported back based on anthropometric characteristics in normotensive and hypertensive participants?
A hybrid PCA-LS-SVM model effectively predicts blood pressure reactivity to an unsupported back using anthropometric characteristics.
May aid noninvasive BP reactivity assessment; leaves open prospective validation before clinical use.
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.
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Kaur et al. (2015) studied Normotension and hypertension. PCA-based LS-SVM model vs. SWR, ANN, and ANFIS models was evaluated on Prediction of blood pressure reactivity to an unsupported back. A PCA-based LS-SVM model was a promising approach for predicting blood pressure reactivity to an unsupported back compared to other hybrid models.
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