An optimized support vector machine for lung cancer classification system
The performance of the system is evaluated in terms of false-positive rate (FPR), sensitivity, specificity, recognition time and recognition accuracy.
Authors
- Olufemi S. Ojo (ORCID: https://orcid.org/0000-0001-7505-7990)
- Abidemi Emmanuel Adeniyi
- Oluwasegun Julius Aroba (ORCID: https://orcid.org/0000-0002-3693-7255)
- Mayowa O. Oyediran (ORCID: https://orcid.org/0000-0001-6756-1476)
- Ibrahim A. Raji
Institutions
- Durban University of Technology (ZA)
- Bowen University (NG)
- University of Johannesburg (ZA)
- Ajayi Crowther University (NG)
- Department of Mathematical Sciences (RU)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-12-23
- DOI
- https://doi.org/10.3389/fonc.2024.1408199
- Citations
- 7
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 1.64