Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithm

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 algorithm...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 9483 - 19
Hauptverfasser: M. El-Kenawy, El-Sayed, Khodadadi, Nima, Eid, Marwa M., Khodadadi, Ehsaneh, Khodadadi, Ehsan, Khafaga, Doaa Sami, Alhussan, Amel Ali, Ibrahim, Abdelhameed, Saber, Mohamed
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 19.03.2025
Nature Publishing Group
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631/67/1347
639/705/117
Al-Biruni Earth radius optimization algorithm
Algorithms
Cancer
Cancer treatment
Decision making
Feature selection
Humanities and Social Sciences
Humans
Learning algorithms
Machine Learning
Medical dataset
Medical technology
multidisciplinary
Neoplasms - diagnosis
Performance assessment
Science
Science (multidisciplinary)
Statistical analysis
Variance analysis
Feature selection
Al-Biruni Earth radius optimization algorithm
Medical dataset
Cancer treatment
ISSN:2045-2322, 2045-2322
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Zusammenfassung: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-92187-2