Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithm
Abstract With the advancement of medical technology, a large amount of complex data on cancers is produced for diagnosing and treating cancers. However, not all this data is useful, as many features are redundant or irrelevant, which can reduce the accuracy of machine learning models. Metaheuristic...
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Nature Portfolio
2025-03-01
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| Online Access: | https://doi.org/10.1038/s41598-025-92187-2 |
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| author | El-Sayed M. El-Kenawy Nima Khodadadi Marwa M. Eid Ehsaneh Khodadadi Ehsan Khodadadi Doaa Sami Khafaga Amel Ali Alhussan Abdelhameed Ibrahim Mohamed Saber |
| author_facet | El-Sayed M. El-Kenawy Nima Khodadadi Marwa M. Eid Ehsaneh Khodadadi Ehsan Khodadadi Doaa Sami Khafaga Amel Ali Alhussan Abdelhameed Ibrahim Mohamed Saber |
| author_sort | El-Sayed M. El-Kenawy |
| collection | DOAJ |
| description | Abstract With the advancement of medical technology, a large amount of complex data on cancers is produced for diagnosing and treating cancers. However, not all this data is useful, as many features are redundant or irrelevant, which can reduce the accuracy of machine learning models. Metaheuristic algorithms have been employed to select features to address this issue. Although the efficacy of these algorithms has been demonstrated, challenges related to scalability and efficiency persist when handling large medical datasets. In this study, a binary version of the Advanced Al-Biruni Earth Radius (bABER) algorithm is proposed for the intelligent removal of unnecessary data and identifying the most essential features for cancer detection. Unlike traditional methods that rely on a single approach, bABER is evaluated using seven medical datasets and compared with eight widely used binary metaheuristic algorithms, including bSC, bPSO, bWAO, bGWO, bMVO, bSBO, bFA, and bGA. Statistical tests such as ANOVA and the Wilcoxon signed-rank test are conducted to ensure a thorough performance assessment. The results indicate that the bABER algorithm significantly outperforms other methods, making it a valuable tool for improving cancer diagnosis. By refining feature selection, this approach enhances existing machine learning models, leading to more accurate and reliable medical predictions. This study contributes to improved data-driven decision-making in healthcare, bringing the field closer to faster and more precise cancer detection. |
| format | Article |
| id | doaj-art-ec86cebc25834b80af4a120cecb2067d |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ec86cebc25834b80af4a120cecb2067d2025-08-20T02:41:34ZengNature PortfolioScientific Reports2045-23222025-03-0115111910.1038/s41598-025-92187-2Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithmEl-Sayed M. El-Kenawy0Nima Khodadadi1Marwa M. Eid2Ehsaneh Khodadadi3Ehsan Khodadadi4Doaa Sami Khafaga5Amel Ali Alhussan6Abdelhameed Ibrahim7Mohamed Saber8School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain PolytechnicDepartment of Civil and Architectural Engineering, University of MiamiFaculty of Artificial Intelligence, Delta University for Science and TechnologyDepartment of Chemistry and Biochemistry, University of ArkansasDepartment of Chemistry and Biochemistry, University of ArkansasDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversitySchool of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain PolytechnicElectronics and Communications Engineering Department, Faculty of Engineering, Delta University for Science and TechnologyAbstract With the advancement of medical technology, a large amount of complex data on cancers is produced for diagnosing and treating cancers. However, not all this data is useful, as many features are redundant or irrelevant, which can reduce the accuracy of machine learning models. Metaheuristic algorithms have been employed to select features to address this issue. Although the efficacy of these algorithms has been demonstrated, challenges related to scalability and efficiency persist when handling large medical datasets. In this study, a binary version of the Advanced Al-Biruni Earth Radius (bABER) algorithm is proposed for the intelligent removal of unnecessary data and identifying the most essential features for cancer detection. Unlike traditional methods that rely on a single approach, bABER is evaluated using seven medical datasets and compared with eight widely used binary metaheuristic algorithms, including bSC, bPSO, bWAO, bGWO, bMVO, bSBO, bFA, and bGA. Statistical tests such as ANOVA and the Wilcoxon signed-rank test are conducted to ensure a thorough performance assessment. The results indicate that the bABER algorithm significantly outperforms other methods, making it a valuable tool for improving cancer diagnosis. By refining feature selection, this approach enhances existing machine learning models, leading to more accurate and reliable medical predictions. This study contributes to improved data-driven decision-making in healthcare, bringing the field closer to faster and more precise cancer detection.https://doi.org/10.1038/s41598-025-92187-2Medical datasetFeature selectionAl-Biruni Earth radius optimization algorithmCancer treatment |
| spellingShingle | El-Sayed M. El-Kenawy Nima Khodadadi Marwa M. Eid Ehsaneh Khodadadi Ehsan Khodadadi Doaa Sami Khafaga Amel Ali Alhussan Abdelhameed Ibrahim Mohamed Saber Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithm Scientific Reports Medical dataset Feature selection Al-Biruni Earth radius optimization algorithm Cancer treatment |
| title | Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithm |
| title_full | Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithm |
| title_fullStr | Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithm |
| title_full_unstemmed | Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithm |
| title_short | Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithm |
| title_sort | improved cancer detection through feature selection using the binary al biruni earth radius algorithm |
| topic | Medical dataset Feature selection Al-Biruni Earth radius optimization algorithm Cancer treatment |
| url | https://doi.org/10.1038/s41598-025-92187-2 |
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