scholarly journals Support Vector Regression-Based Recursive Ensemble Methodology for Confidence Interval Estimation in Blood Pressure Measurements

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Soojeong Lee ◽  
Gangseong Lee
Keyword(s):  
Blood Pressure ◽  
Support Vector Machine ◽  
Confidence Interval ◽  
Interval Estimation ◽  
Absolute Error ◽  
Support Vector ◽  
Measured Quantity ◽  
Pressure Measurements ◽  
Measure Blood Pressure ◽  
A New Technique

The monitors of oscillometry blood pressure measurements are generally utilized to measure blood pressure for many subjects at hospitals, homes, and office, and they are actively studied. These monitors usually provide a single blood pressure point, and they are not able to indicate the confidence interval of the measured quantity. In this paper, we propose a new technique using a recursive ensemble based on a support vector machine to estimate a confidence interval for oscillometry blood pressure measurements. The recursive ensemble is based on a support vector machine that is used to effectively estimate blood pressure and then measure the confidence interval for the systolic blood pressure and diastolic blood pressure. The recursive ensemble methodology provides a lower standard deviation of error, mean error, and mean absolute error for the blood pressure as compared to those of the conventional techniques.

scholarly journals Uncertainty in Blood Pressure Measurement Estimated Using Ensemble-Based Recursive Methodology

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2108
Author(s):  
Soojeong Lee ◽  
Hilmi R Dajani ◽  
Sreeraman Rajan ◽  
Gangseong Lee ◽  
Voicu Z Groza
Keyword(s):  
Blood Pressure ◽  
Blood Pressure Measurement ◽  
Measured Quantity ◽  
Home Office ◽  
Medical Centers ◽  
Measure Blood Pressure ◽  
Third Stage ◽  
Blood Pressure Monitors ◽  
A New Technique ◽  
Fine Tune

Automated oscillometric blood pressure monitors are commonly used to measure blood pressure for many patients at home, office, and medical centers, and they have been actively studied recently. These devices usually provide a single blood pressure point and they are not able to indicate the uncertainty of the measured quantity. We propose a new technique using an ensemble-based recursive methodology to measure uncertainty for oscillometric blood pressure measurements. There are three stages we consider: the first stage is pre-learning to initialize good parameters using the bagging technique. In the second stage, we fine-tune the parameters using the ensemble-based recursive methodology that is used to accurately estimate blood pressure and then measure the uncertainty for the systolic blood pressure and diastolic blood pressure in the third stage.

Confidence Interval Estimation for Oscillometric Blood Pressure Measurements Using Bootstrap Approaches

2011 ◽  
Vol 60 (10) ◽  
pp. 3405-3415 ◽  
Author(s):  
Soojeong Lee ◽  
Miodrag Bolic ◽  
Voicu Z. Groza ◽  
Hilmi R. Dajani ◽  
Sreeraman Rajan
Keyword(s):  
Blood Pressure ◽  
Confidence Interval ◽  
Interval Estimation ◽  
Pressure Measurements ◽  
Confidence Interval Estimation

Estimating Interval of the Number of Errors for Embedded Software Development Projects

2014 ◽  
Vol 2 (3) ◽  
pp. 40-50 ◽  
Author(s):  
Kazunori Iwata ◽  
Toyoshiro Nakasima ◽  
Yoshiyuki Anan ◽  
Naohiro Ishii
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Software Development ◽  
Previous Investigation ◽  
Interval Estimation ◽  
Embedded Software ◽  
Point Estimation ◽  
Support Vector ◽  
Development Projects ◽  
Artificial Neural Network Ann

Previous investigation focused on the prediction of total and errors for embedded software development projects using an artificial neural network (ANN). However, methods using ANNs have reached their improvement limits, since an appropriate value is estimated using what is known as point estimation in statistics. This paper proposes a method for predicting the number of errors for embedded software development projects using interval estimation provided by a support vector machine and ANN.

scholarly journals Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Seyed Reza Kamel ◽  
Reyhaneh YaghoubZadeh ◽  
Maryam Kheirabadi
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Support Vector Machine ◽  
Feature Selection Method ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Gray Wolf ◽  
Diagnose Breast Cancer ◽  
Computer Methods ◽  
A New Technique

Abstract One of the most common diseases among women is breast cancer, the early diagnosis of which is of paramount importance. Given the time-consuming nature of the diagnosis process of the disease, using new methods such as computer science is extremely important for early detection of the condition. Today, the main emphasis is on the science of data mining as one of the computer methods in the field of diagnosis. In the present study, we used data mining as a combination of feature selection method by Gray Wolf Optimization (GWO) and support vector machine (SVM), which is a new technique with high accuracy compared to other methods in this classification, to increase the accuracy of breast cancer diagnosis. The UCI dataset and functional parameters and various statistical criteria were applied to evaluate the proposed method and assess the validity of the results in MATLAB, respectively. Application of the proposed method increased the improvement of the evaluated criteria, which increased the accuracy of diagnosis by 27.68%, compared to former works in the field. As such, it could be concluded that the proposed method had a higher ability to diagnose breast cancer, compared to previous techniques.

Blood Pressure Measurement Error in Children: Lessons in Measurement Reliability

2019 ◽  
Vol 27 (1) ◽  
pp. 114-125 ◽  
Author(s):  
Esther J. Varney ◽  
Ashley M. Van Drunen ◽  
Emily F. Moore ◽  
Kristen Carlin ◽  
Karen Thomas
Keyword(s):  
Blood Pressure ◽  
Measurement Error ◽  
Pressure Measurement ◽  
Blood Pressure Measurement ◽  
Pressure Measurements ◽  
Noninvasive Blood Pressure ◽  
Measure Blood Pressure ◽  
Noninvasive Blood Pressure Measurement ◽  
Manual Blood Pressure ◽  
Acceptable Agreement

Background and PurposeBlood pressure measurement represents the pressure exerted during heart ejection and filling. There are several ways to measure blood pressure and a valid measure is essential. The purpose of this study was to evaluate the approach to noninvasive blood pressure measurement in children.MethodsBlood pressure measurements were taken using the automatic Phillips MP30 monitor and compared against Welch Allyn blood pressure cuffs with Medline manual sphygmomanometers.ResultsA total of 492 measurements were taken on 82 subjects, and they demonstrated comparability between automatic and manual devices.ConclusionsAlthough our study indicated acceptable agreement between automatic and manual blood pressure measurement, it also revealed measurement error remains a concern, with sample size, study protocol, training, and environment all playing a role.

Coke Rate Prediction Model Based on Genetic Algorithm Optimized Support Vector Machine

2015 ◽  
Vol 740 ◽  
pp. 600-603
Author(s):  
You Jun Yue ◽  
Yan Fei Hu ◽  
Hui Zhao ◽  
Hong Jun Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Absolute Error ◽  
Support Vector ◽  
Model Parameters ◽  
Coke Rate ◽  
Rate Model ◽  
Iron And Steel ◽  
Model Based ◽  
Blast Furnace Coke

The accurate prediction model’s establishing of the blast furnace coke rate is important for optimizing the integrated production indicators of iron and steel enterprise. For the problem of accuracy of the model of coke rate, This paper established blast coke rate modeling with support vector machine algorithm, the model parameters of support vector machine was optimized by genetic algorithm, then a coke rate model based on support vector machine with the best parameters was built. Simulation results showed that: the forecasting model’s outcome, average absolute error and the mean relative error, was small which is based on genetic algorithm optimized SVM. coke rate model based on Genetic algorithm optimized support vector machine has high degree of accuracy and a certain practicality.

Prediction of Undervoltage Load Shedding Using Quantum-Inspired Evolutionary Programming-Support Vector Machine

2015 ◽  
Vol 785 ◽  
pp. 43-47
Author(s):  
Zuhaila Mat Yasin ◽  
Zuhaina Zakaria ◽  
Titik Khawa Abdul Rahman
Keyword(s):  
Support Vector Machine ◽  
Computation Time ◽  
Evolutionary Programming ◽  
Test System ◽  
Least Square ◽  
Support Vector ◽  
Rbf Kernel ◽  
Validation Procedure ◽  
Programming Support ◽  
A New Technique

This paper presents a new technique to predict the optimal amount of load to be shed at various loading conditions using Quantum-Inspired Evolutionary Programming–Support Vector Machine (QIEP-SVM). QIEP is utilised to optimise the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimisation is to minimise the mean square error (MSE). The performance of QIEP-SVM technique was compared with those obtained from LS-SVM technique with prediction accuracy through a 10-fold cross-validation procedure. All simulations in this study were carried out using IEEE 69-bus distribution test system. QIEP-SVM model had shown better prediction performance as compared to LS-SVM. The results also indicate that the proposed approach outperforms the most recently reported technique in terms of accuracy and fast computation time.

Diagnostic Methods for Pregnancy Hypertension: Significance of Standardized Conditions

1992 ◽  
Vol 8 (S1) ◽  
pp. 72-74 ◽  
Author(s):  
Anders Åberg
Keyword(s):  
Blood Pressure ◽  
Pregnant Women ◽  
Diastolic Blood Pressure ◽  
Home Monitoring ◽  
Upright Position ◽  
Diagnostic Methods ◽  
Pressure Measurements ◽  
Pregnancy Hypertension ◽  
Measure Blood Pressure ◽  
Risk Of Preeclampsia

AbstractStandardized conditions for blood pressure measurements and strict definitions of systolic and diastolic blood pressure are essential for a consequent management of hypertension during pregnancy. In Sweden, it has been agreed to measure blood pressure with the pregnant women sitting in upright position. Home-monitoring of blood pressure is recommended in women at risk of preeclampsia.

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