ArticleComputational and mathematical methods in medicine2018
Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants.
Article in Computational and mathematical methods in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 104 citations in OpenAlex.
- MORE: a multi-omics data-driven hypergraph integration network for biomedical data classification and biomarker identification.Briefings in bioinformatics · 2024Article
- Article
- Improving performance of classifiers for diagnosis of critical diseases to prevent COVID risk.Computers & electrical engineering : an international journal · 2022Article
- Multiclass feature selection with metaheuristic optimization algorithms: a review.Neural computing & applications · 2022Review
- Identifiable Patterns of Trait, State, and Experience in Chronic Stroke Recovery.Neurorehabilitation and neural repair · 2021Article
- Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model.iScience · 2020Article
- Prediction of Protein-Protein Interactions Based on Domain.Computational and mathematical methods in medicine · 2019Article
- Arrhythmia Classification of ECG Signals Using Hybrid Features.Computational and mathematical methods in medicine · 2018Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving lives. This study is conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other fifteen classes represent electrocardiogram records of various subtypes of arrhythmias. The research is carried out on the dataset taken from the University of California at Irvine Machine Learning Data Repository. The dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique. For multiclass classification, support vector machine (SVM) based approaches including one-against-one (OAO), one-against-all (OAA), and error-correction code (ECC) are employed to detect the presence and absence of arrhythmias. The SVM method results are compared with other standard machine learning classifiers using varying parameters and the performance of the classifiers is evaluated using accuracy, kappa statistics, and root mean square error. The results show that OAO method of SVM outperforms all other classifiers by achieving an accuracy rate of 81.11% when used with 80/20 data split and 92.07% using 90/10 data split option.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.