ArticleHealthcare technology letters2019
Prediction of cancer using customised fuzzy rough machine learning approaches.
Article in Healthcare technology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Breast Cancer Detection Using Convolutional Neural Networks: A Deep Learning-Based Approach.Cureus · 2025Review
- Optimal control methods for drug delivery in cancerous tumour by anti-angiogenic therapy and chemotherapy.IET systems biology · 2021Article
- Developing a clinical decision support system based on the fuzzy logic and decision tree to predict colorectal cancer.Medical journal of the Islamic Republic of Iran · 2021Article
- Designing a Resilient Controller for Cancer Immunotherapy: Application to a Fractional-Order Tumour-Immune Model.IET systems biologyArticle
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This Letter proposes a customised approach for attribute selection applied to the fuzzy rough quick reduct algorithm. The unbalanced data is balanced using synthetic minority oversampling technique. The huge dimensionality of the cancer data is reduced using a correlation-based filter. The dimensionality reduced balanced attribute gene subset is used to compute the final minimal reduct set using a customised fuzzy triangular norm operator on the fuzzy rough quick reduct algorithm. The customised fuzzy triangular norm operator is used with a Lukasiewicz fuzzy implicator to compute the fuzzy approximation. The customised operator selects the least number of informative feature genes from the dimensionality reduced datasets. Classification accuracy using leave-one-out cross validation of 94.85, 76.54, 98.11, and 99.13% is obtained using a customised function for Lukasiewicz triangular norm operator on leukemia, central nervous system, lung, and ovarian datasets, respectively. Performance analysis of the conventional fuzzy rough quick reduct and the proposed method are performed using parameters such as classification accuracy, precision, recall,
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