Comput Math Methods Med - Prediction of BP reactivity to talking using hybrid soft computing approaches.

Tópicos

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Resumo

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 (R (2)), 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.

Resumo Limpo

high blood pressur bp associ increas risk cardiovascular diseas therefor optim precis measur bp appropri clinic research studi work anthropometr characterist includ age height weight bodi mass index bmi arm circumfer ac use independ predictor variabl predict bp reactiv talk princip compon analysi pca fuse artifici neural network ann adapt neurofuzzi infer system anfi least squaresupport vector machin lssvm model remov multicollinear effect among anthropometr predictor variabl statist test term coeffici determin r root mean squar error rmse mean absolut percentag error mape reveal pca base lssvm pcalssvm model produc effici predict bp reactiv compar model assess present import advantag pose pca fuse predict model predict biolog variabl

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