ArticleMedical & biological engineering & computing2020
Medical data set classification using a new feature selection algorithm combined with twin-bounded support vector machine.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Tuina therapy alleviates knee osteoarthritis by modulating PI3K/AKT/mTOR-mediated autophagy: an integrated machine learning andFrontiers in immunology · 2025Article
- Research on early warning of renal damage in hypertensive patients based on the stacking strategy.BMC medical informatics and decision making · 2022Article
- Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis.Knowledge-based systems · 2022Article
- A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification.Sensors (Basel, Switzerland) · 2021Article
- BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm.PeerJ. Computer science · 2021Article
- The Influencing Legal and Factors of Migrant Children's Educational Integration Based on Convolutional Neural Network.Frontiers in psychology · 2021Article
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Authors and funding
3 authors.
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Abstract
Early diagnosis and treatment are the most important strategies to prevent deaths from several diseases. In this regard, data mining and machine learning techniques have been useful tools to help minimize errors and to provide useful information for diagnosis. Our paper aims to present a new feature selection algorithm. In order to validate our study, we used eight benchmark data sets which are commonly used among researchers who developed machine learning methods for medical data classification. The experiment has shown that the performance of our proposed new feature selection method combined with twin-bounded support vector machine (FSTBSVM) is very efficient. The robustness of the FSTBSVM is examined using classification accuracy, analysis of sensitivity, and specificity. The proposed FSTBSVM is a very promising technique for classification, and the results show that the proposed method is capable of producing good results with fewer features than the original data sets. Graphical abstract Model using a new feature selection and grid search with 10-fold CV to optimize model parameters in our FSTBSVM.
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