Key result
A PCA-based LS-SVM model accurately predicted BP reactivity to crossed legs, achieving an R2 of 93.16% for SBP in normotensive subjects and 96.46% for SBP in hypertensive subjects.
Why the study?
Do hybrid computing models accurately predict blood pressure reactivity to crossed legs in normotensive and hypertensive participants?
Do hybrid computing models accurately predict blood pressure reactivity to crossed legs in normotensive and hypertensive participants?
The PCA-LS-SVM hybrid computing model accurately predicts blood pressure reactivity to crossed legs, highlighting the utility of hybrid models for interpreting physiological stimuli during BP measurement.
May support hybrid models for BP reactivity assessment; leaves open prospective validation before clinical adoption.
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 ( R 2 ) = 93.16%, root mean square error (RMSE) = 0.27, and mean absolute percentage error (MAPE) = 5.71 for SBP prediction in normotensive subjects. Furthermore, R 2 = 96.46%, RMSE = 0.19, and MAPE = 1.76 for SBP prediction and R 2 = 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.
No takes yet. Share an insight, caveat, or question.
Kaur et al. (2017) studied Blood pressure reactivity to crossed legs. PCA-based LS-SVM model vs. FSWR, ANN, and ANFIS models was evaluated on Prediction accuracy of BP reactivity to crossed legs. A PCA-based LS-SVM model accurately predicted BP reactivity to crossed legs, achieving an R2 of 93.16% for SBP in normotensive subjects and 96.46% for SBP in hypertensive subjects.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: