Abstract
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.
Highlights
Accurate measurement of blood pressure (BP) is indispensable for the diagnosis of hypertension at its early stage
The results of the paired t-test demonstrated a statistically significant higher systolic BP (SBP) with crossed legs in normotensive subjects, but there was no significant difference between diastolic BP (DBP) measurements
These results are consistent with the recommendations of the American Heart Association (AHA) council for BP measurement in humans and experimental animals [3]
Summary
Accurate measurement of blood pressure (BP) is indispensable for the diagnosis of hypertension at its early stage. There is clear recognition of biological variability, we continue to make decisions largely on measurements taken at random times under poorly controlled conditions” [2]. This observation supports the need to develop novel methods for accurate prediction of BP. Recommendations of several international organisations including the AHA [3], British Hypertension Society (BHS) [4], and European Society of Hypertension (ESH) [5] revealed that BP is influenced by numerous biological and analytical sources of variation. Analytical variations are derived from the variability of the instrument used, observer bias, and so forth. It is not always feasible to control all the factors, but we can minimize their effect by taking them into account in reaching a decision [5]
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