ArticleJournal of medical systems2014
An intelligent system for lung cancer diagnosis using a new genetic algorithm based feature selection method.
Article in Journal of medical systems, 2014. 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.
- Deterministic solution of algebraic equations in sentiment analysis.Multimedia tools and applications · 2023Article
- Discovery of Knowledge in the Incidence of a Type of Lung Cancer for Patients through Data Mining Models.Computational intelligence and neuroscience · 2022Article
- Article
- Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric.Journal of medical systems · 2017Article
- Hypergraph Based Feature Selection Technique for Medical Diagnosis.Journal of medical systems · 2016Article
- Three-dimensional SVM with latent variable: application for detection of lung lesions in CT images.Journal of medical systems · 2015Article
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Authors and funding
3 authors.
Funding
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Abstract
In this paper, we develop a novel feature selection algorithm based on the genetic algorithm (GA) using a specifically devised trace-based separability criterion. According to the scores of class separability and variable separability, this criterion measures the significance of feature subset, independent of any specific classification. In addition, a mutual information matrix between variables is used as features for classification, and no prior knowledge about the cardinality of feature subset is required. Experiments are performed by using a standard lung cancer dataset. The obtained solutions are verified with three different classifiers, including the support vector machine (SVM), the back-propagation neural network (BPNN), and the K-nearest neighbor (KNN), and compared with those obtained by the whole feature set, the F-score and the correlation-based feature selection methods. The comparison results show that the proposed intelligent system has a good diagnosis performance and can be used as a promising tool for lung cancer diagnosis.
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Registered trials
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