title: Prediction of BP reactivity to talking using hybrid soft computing approaches.
abstract: 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.
authors.affiliation: Electrical and Instrumentation Engineering Department, Sant Longowal Institute of Engineering & Technology, Deemed University (Established by Government of India), Longowal, Sangrur District, Punjab 148106, India.
authors.forename: Gurmanik
authors.identifier: 0000-0002-6384-4396
authors.initials: G
authors.lastname: Kaur
authors.affiliation: Electrical and Instrumentation Engineering Department, Sant Longowal Institute of Engineering & Technology, Deemed University (Established by Government of India), Longowal, Sangrur District, Punjab 148106, India.
authors.forename: Ajat Shatru
authors.initials: AS
authors.lastname: Arora
authors.affiliation: Electrical and Instrumentation Engineering Department, Sant Longowal Institute of Engineering & Technology, Deemed University (Established by Government of India), Longowal, Sangrur District, Punjab 148106, India.
authors.forename: Vijender Kumar
authors.initials: VK
authors.lastname: Jain
mesh_terms: D000367:Age Factors; D000886:Anthropometry; D001794:Blood Pressure; D001827:Body Height; D015992:Body Mass Index; D001835:Body Weight; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D006973:Hypertension / Q000175:diagnosis*; D016018:Least-Squares Analysis; D015233:Models, Statistical; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D014705:Verbal Behavior