Diagnosis of cardiac arrhythmia using kernel difference weighted KNN classifier

Zuo, Wangmeng; Lu, Wen-Yang; Wang, K.Q.; Zhang, H.

doi:10.1109/cic.2008.4749025

Cited by 54 publications

(18 citation statements)

References 6 publications

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“…From the comparison chart shown in fig.3, it is observed that our neuro-genetic model outperformed on selected feature subset compared to full feature set. A novel pruning method [14 ] 68.47% Kernel Difference weighted KNN [15] 70.66% SVM with Gaussian Kernel [16] 76.10% Learning Vector Quantization [17] 76.92% Fuzzy weighted AIRS [18] 80.71% NN with weighted fuzzy membership function [19] 81.32% Modular Neural Network [20] 82.22% MLP with two hidden layers [21] 85.55% Proposed Neuro-Genetic Approach 89.58%…”

Section: Resultsmentioning

confidence: 99%

Dimensionality reduction using neuro-genetic approach for early prediction of coronary heart disease

Murthy

Meenakshi²

2014

International Conference on Circuits, Communication, Control and Computing

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This paper presents the development of a Neurogenetic model for the prediction of coronary heart diseases. The novelty of this work is feature subset selection using multiobjective genetic algorithm without sacrificing the accuracy of ANN based heart disease predictor. Subsequently, the selected feature subset is used to predict the level of angiographic coronary heart disease using neural networks. The performance of the developed Neuro-Genetic model is evaluated using heart disease database obtained from Cleveland Clinic Foundation Database with all attributes are numeric-valued. The accuracy of the designed Neruo-Genetic model is validated using 303 patient data sets obtained for different age groups. This study exhibits early detection of heart disease with high testing accuracy of 89.58% through minimized feature subset, thereby reducing the complexity.

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Section: Resultsmentioning

confidence: 99%

Dimensionality reduction using neuro-genetic approach for early prediction of coronary heart disease

Murthy

Meenakshi²

2014

International Conference on Circuits, Communication, Control and Computing

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“…Compared to algorithms which utilized voting feature intervals, naive Bayes, k-nearest neighbors (KNN), support vector machines (SVM), and logistic regression, the model proposed here offers increased accuracy [6,[18][19][20].…”

Section: Discussionmentioning

confidence: 99%

Classification and Diagnosis of Cardiac Arrhythmia using an ECG-based Ensemble Approach

Agarwal

2018

JBEMI

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Cardiovascular Disease (CVD) remains the leading cause of death, worldwide and in the United States. Approximately 30% of global deaths can be attributed to one form of CVD, including conditions such as heart disease, stroke, heart attack, and arrhythmia. In diagnosing CVD, electrocardiograms (ECG) are commonly used to measure and record the electrical activity of the heart. Their non-invasive, informative, and relatively simple nature allows for rapid deployment. However, because analysis of ECGs depends solely on a physician, ECG analysis becomes subjective, adding a potential layer of error to patient healthcare. Studies indicate that physicians often misread ECGs and disagree with each other's interpretations. In order to develop an accurate and objective method for ECG analysis, this study evaluates various ensemble algorithms to design and create a supervised classification model. Several ensemble models were evaluated to derive one which correctly classifies CVD with sufficiently high accuracy. A boosted decision tree ensemble created to evaluate cardiac condition performs best, with an overall accuracy of 84.6% and an AUC of 0.828.

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“…NN's error rate has been proven to be asymptotically at most twice that of the Bayesian error rate [20]. NN has been used for image clustering [21] and cardiac arrhythmia diagnosis [22], and so forth, but few published researches can be found in machine fault detection. In this paper, the NN classifier is used to evaluate the reduced feature vectors by KPCA for avoiding relatively local relevance between parameters and reducing computation complexity.…”

Section: Multiclasses Fault Diagnosis -Nearest Neighbormentioning

confidence: 99%

NC Machine Tools Fault Diagnosis Based on Kernel PCA andk-Nearest Neighbor Using Vibration Signals

Zhou

Bingtao

et al. 2015

Shock and Vibration

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This paper focuses on the fault diagnosis for NC machine tools and puts forward a fault diagnosis method based on kernel principal component analysis (KPCA) andk-nearest neighbor (kNN). A data-dependent KPCA based on covariance matrix of sample data is designed to overcome the subjectivity in parameter selection of kernel function and is used to transform original high-dimensional data into low-dimensional manifold feature space with the intrinsic dimensionality. ThekNN method is modified to adapt the fault diagnosis of tools that can determine thresholds of multifault classes and is applied to detect potential faults. An experimental analysis in NC milling machine tools is developed; the testing result shows that the proposed method is outperforming compared to the other two methods in tool fault diagnosis.

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Diagnosis of cardiac arrhythmia using kernel difference weighted KNN classifier

Abstract: In this paper, we proposed a kernel differenceweighted k-nearest neighbor classifier (KDF-WKNN)

Cited by 54 publications

References 6 publications

Dimensionality reduction using neuro-genetic approach for early prediction of coronary heart disease

Dimensionality reduction using neuro-genetic approach for early prediction of coronary heart disease

Classification and Diagnosis of Cardiac Arrhythmia using an ECG-based Ensemble Approach

NC Machine Tools Fault Diagnosis Based on Kernel PCA andk-Nearest Neighbor Using Vibration Signals

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