Predicting Student Blood Pressure by Support Vector Machine Using Facebook

Khan, Shazada Muhammad Umair; Manzoor, Javeria Shaikh; Lee, Scott Uk Jin

doi:10.1109/services.2014.92

Cited by 2 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The outcome showed that SVM can be prosperously applied for prediction of BP through primary emotions. On the contrary, validations signified that the error statistics of the SVM model marginally outperformed [ 32 ]. Barbe et al developed a logistic regression model to calibrate and correct an oscillometric monitor such that the device better corresponds to the Korotkoff method regardless of the health status of the patient.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Hybrid Models for Prediction of BP Reactivity to Crossed Legs

Kaur¹,

Arora

Jain

2017

Journal of Healthcare Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Hybrid Models for Prediction of BP Reactivity to Crossed Legs

Kaur¹,

Arora

Jain

2017

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…The results showed that SVM and permanental classification both outperformed logistic regression. Khan et al (2014) proposed SVM for performing the prediction of BP with primary emotions using Facebook status. Current human BP and those belonging to up to six previous primary emotions were given as input variables.…”

Section: Introductionmentioning

confidence: 99%

Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Kaur

Arora

Jain

2015

Frontiers Inf Technol Electronic Eng

View full text Add to dashboard Cite

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.

show abstract

Predicting Student Blood Pressure by Support Vector Machine Using Facebook

Cited by 2 publications

References 25 publications

Comparative Analysis of Hybrid Models for Prediction of BP Reactivity to Crossed Legs

Comparative Analysis of Hybrid Models for Prediction of BP Reactivity to Crossed Legs

Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Contact Info

Product

Resources

About