- Title
- Cancer classification utilizing voting classifier with ensemble feature selection method and transcriptomic data
- Creator
- Khatun, Rabea; Akter, Maksuda; Islam, Md Manowarul; Uddin, Md Ashraf; Talukder, Md Alamin; Kamruzzaman, Joarder; Azad, Akm; Paul, Bikash; Almoyad, Muhammad; Aryal, Sunil; Moni, Mohammad
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/197686
- Identifier
- vital:18915
- Identifier
-
https://doi.org/10.3390/genes14091802
- Identifier
- ISSN:2073-4425 (ISSN)
- Abstract
- Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods. © 2023 by the authors.
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Relation
- Genes Vol. 14, no. 9 (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2023 by the authors
- Rights
- Open Access
- Subject
- 3105 Genetics; Cancer detection; Feature selection; Gene analysis; Gene data; Machine learning; Voting classifier
- Full Text
- Reviewed
- Funder
- This research was supported by the Deanship of Scientific Research Large Groups at King Khalid University, Kingdom of Saudi Arabia (RGP.2/219/43).
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