Twin support vector machines: A survey

Huang, Huajuan; Wei, Xiuxi; Zhou, Yongquan

doi:10.1016/j.neucom.2018.01.093

Cited by 51 publications

(18 citation statements)

References 131 publications

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“…ese regression models are trained using 50,000 to 100,000 samples of vital signs recorded within an interval of 10 milliseconds. ese forecasted values are then assigned to SVM [14], Decision Tree [15], and Naive Bayes [16] classifiers to predict the upcoming condition of a patient. e results revealed that the decision tree had obtained commendable prediction results.…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases

Shah

Aleem

Iqbal

et al. 2021

Journal of Healthcare Engineering

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Cardiovascular and chronic respiratory diseases are global threats to public health and cause approximately 19 million deaths worldwide annually. This high mortality rate can be reduced with the use of technological advancements in medical science that can facilitate continuous monitoring of physiological parameters—blood pressure, cholesterol levels, blood glucose, etc. The futuristic values of these critical physiological or vital sign parameters not only enable in-time assistance from medical experts and caregivers but also help patients manage their health status by receiving relevant regular alerts/advice from healthcare practitioners. In this study, we propose a machine-learning-based prediction and classification system to determine futuristic values of related vital signs for both cardiovascular and chronic respiratory diseases. Based on the prediction of futuristic values, the proposed system can classify patients’ health status to alarm the caregivers and medical experts. In this machine-learning-based prediction and classification model, we have used a real vital sign dataset. To predict the next 1–3 minutes of vital sign values, several regression techniques (i.e., linear regression and polynomial regression of degrees 2, 3, and 4) have been tested. For caregivers, a 60-second prediction and to facilitate emergency medical assistance, a 3-minute prediction of vital signs is used. Based on the predicted vital signs values, the patient’s overall health is assessed using three machine learning classifiers, i.e., Support Vector Machine (SVM), Naive Bayes, and Decision Tree. Our results show that the Decision Tree can correctly classify a patient’s health status based on abnormal vital sign values and is helpful in timely medical care to the patients.

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Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases

Shah

Aleem

Iqbal

et al. 2021

Journal of Healthcare Engineering

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show abstract

“…The parameter σ is the standard deviation of data points. Using the standard Lagrange multiplier method is the common way to solve this kind of optimization problem (The detailed explanation about the solution can be found in [22]).…”

Section: Twsvm: Wrapper Phase and Classification Methodsmentioning

confidence: 99%

Modularized Filter-Wrapper Feature Selection Scheme for Finding Optimal Image Moments in Maps of Radio Galaxies

2021

IJIES

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“…If its distance to the positive hyper-plane is less than its distance from the negative hyper-plane, it belongs to the positive class. Otherwise it belongs to the negative class [ 34 , 35 , 36 , 37 , 38 ]. Based on the above principle, the classifier can be used to quickly and effectively divide the heart sound signals into two categories: normal and abnormal.…”

Section: The Proposed Algorithm Based On Wavelet Fractal and Twsvmmentioning

confidence: 99%

Classification of Heart Sounds Based on the Wavelet Fractal and Twin Support Vector Machine

2019

Entropy

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Heart is an important organ of human beings. As more and more heart diseases are caused by people’s living pressure or habits, the diagnosis and treatment of heart diseases also require technical improvement. In order to assist the heart diseases diagnosis, the heart sound signal is used to carry a large amount of cardiac state information, so that the heart sound signal processing can achieve the purpose of heart diseases diagnosis and treatment. In order to quickly and accurately judge the heart sound signal, the classification method based on Wavelet Fractal and twin support vector machine (TWSVM) is proposed in this paper. Firstly, the original heart sound signal is decomposed by wavelet transform, and the wavelet decomposition coefficients of the signal are extracted. Then the two-norm eigenvectors of the heart sound signal are obtained by solving the two-norm values of the decomposition coefficients. In order to express the feature information more abundantly, the energy entropy of the decomposed wavelet coefficients is calculated, and then the energy entropy characteristics of the signal are obtained. In addition, based on the fractal dimension, the complexity of the signal is quantitatively described. The box dimension of the heart sound signal is solved by the binary box dimension method. So its fractal dimension characteristics can be obtained. The above eigenvectors are synthesized as the eigenvectors of the heart sound signal. Finally, the twin support vector machine (TWSVM) is applied to classify the heart sound signals. The proposed algorithm is verified on the PhysioNet/CinC Challenge 2016 heart sound database. The experimental results show that this proposed algorithm based on twin support vector machine (TWSVM) is superior to the algorithm based on support vector machine (SVM) in classification accuracy and speed. The proposed algorithm achieves the best results with classification accuracy 90.4%, sensitivity 94.6%, specificity 85.5% and F1 Score 95.2%.

show abstract

Twin support vector machines: A survey

Cited by 51 publications

References 131 publications

A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases

A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases

Modularized Filter-Wrapper Feature Selection Scheme for Finding Optimal Image Moments in Maps of Radio Galaxies

Classification of Heart Sounds Based on the Wavelet Fractal and Twin Support Vector Machine

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