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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Published in:	Scientific reports Vol. 15; no. 1; pp. 9483 - 19
Main Authors:	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
Language:	English
Published:	London Nature Publishing Group UK 19.03.2025 Nature Publishing Group Nature Portfolio
Subjects:	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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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.
AbstractList	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. 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. 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.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.
ArticleNumber	9483
Author	Alhussan, Amel Ali Ibrahim, Abdelhameed Eid, Marwa M. M. El-Kenawy, El-Sayed Khafaga, Doaa Sami Khodadadi, Nima Khodadadi, Ehsaneh Khodadadi, Ehsan Saber, Mohamed
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Cites_doi	10.1007/s00500-018-3545-7 10.1002/dac.3700 10.32604/csse.2023.032497 10.3389/fonc.2024.1432869 10.54216/JAIM.070102 10.1007/s10462-020-09882-x 10.3389/fenrg.2022.995236 10.1016/j.eswa.2019.113122 10.1080/10095020.2019.1612600 10.3390/math10234421 10.3390/e23091189 10.1016/j.asoc.2015.07.005 10.1016/j.cie.2021.107250 10.1016/j.knosys.2018.04.025 10.1016/j.knosys.2020.106553 10.1007/s00521-019-04368-6 10.3390/math10122081 10.1097/MD.0000000000040248 10.1016/j.eswa.2016.02.042 10.1007/s00521-016-2473-7 10.1016/j.asoc.2016.01.019 10.1007/s00366-021-01342-6 10.1109/ACCESS.2024.3521002 10.1016/j.asoc.2019.105509 10.1016/j.cor.2005.11.017 10.32604/cmc.2022.030447 10.1007/s42979-021-00687-5 10.32604/cmc.2022.032229 10.54216/JSDGT.030205 10.3390/pr11051502 10.1021/acs.jmedchem.3c02232 10.1111/exsy.13088 10.1016/j.compeleceng.2017.09.001 10.3390/diagnostics14232632 10.1016/j.ejor.2017.08.040 10.32604/csse.2023.034697 10.1145/2791405.2791549 10.1109/INTELCIS.2017.8260031 10.1016/j.energy.2022.124363 10.1109/IBCAST.2019.8667106 10.1016/j.knosys.2012.11.005 10.1016/j.jocs.2017.07.018 10.1016/j.knosys.2020.105746 10.1016/j.matcom.2021.08.013 10.3390/en16031185 10.1016/j.apm.2021.04.018 10.1007/s00500-016-2106-1 10.1007/s10462-020-09860-3 10.1155/2019/2537689 10.32604/cmc.2022.023884 10.1248/bpb.b17-00882 10.1016/j.asoc.2015.07.023 10.1007/s12652-018-1031-9 10.1109/ACCESS.2019.2906757 10.34133/cbsystems.0013 10.1007/s00521-017-2988-6 10.1016/j.swevo.2012.09.002 10.1007/BF01608556 10.1016/j.asoc.2015.10.005 10.1109/TPAMI.2004.105 10.1109/IC3IoT.2018.8668174 10.1109/TNNLS.2022.3165627 10.1016/j.bioorg.2023.106674 10.1007/s42835-021-00961-9 10.54216/FinTech-I.020203
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Keywords	Feature selection Al-Biruni Earth radius optimization algorithm Medical dataset Cancer treatment
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References	X Duan (92187_CR61) 2023; 4 92187_CR2 M El-Sayed (92187_CR40) 2023; 46 FH Rizk (92187_CR48) 2024; 7 92187_CR1 92187_CR6 92187_CR22 92187_CR21 92187_CR65 92187_CR4 92187_CR20 92187_CR64 92187_CR3 92187_CR26 92187_CR25 92187_CR69 92187_CR24 92187_CR68 92187_CR23 92187_CR29 92187_CR28 D Khafaga (92187_CR47) 2022; 73 92187_CR27 T Srikanth (92187_CR56) 2019; 14 92187_CR9 92187_CR8 92187_CR7 92187_CR51 92187_CR50 H Yin (92187_CR66) 2023; 138 92187_CR11 92187_CR55 92187_CR10 92187_CR54 92187_CR53 SJA Kadir (92187_CR5) 2019; 7 S Mirjalili (92187_CR39) 2020 92187_CR52 92187_CR15 92187_CR59 92187_CR14 92187_CR58 92187_CR13 92187_CR57 92187_CR12 92187_CR19 92187_CR18 XY Fu (92187_CR62) 2024; 67 92187_CR17 92187_CR16 H AlEisa (92187_CR44) 2022; 73 M El-Sayed (92187_CR41) 2023; 45 ES El-kenawy (92187_CR45) 2022; 71 92187_CR43 92187_CR42 92187_CR46 Z Yi-wen (92187_CR67) 2018; 41 92187_CR49 S Chen (92187_CR60) 2024; 14 H Huang (92187_CR63) 2024; 103 92187_CR33 92187_CR32 92187_CR31 92187_CR30 92187_CR37 92187_CR36 92187_CR35 92187_CR34 92187_CR38
References_xml	– ident: 92187_CR22 doi: 10.1007/s00500-018-3545-7 – ident: 92187_CR33 doi: 10.1002/dac.3700 – volume: 45 start-page: 1917 issue: 2 year: 2023 ident: 92187_CR41 publication-title: Comput. Syst. Sci. Eng. doi: 10.32604/csse.2023.032497 – volume: 14 start-page: 1432869 year: 2024 ident: 92187_CR60 publication-title: Front. Oncol. doi: 10.3389/fonc.2024.1432869 – volume: 7 start-page: 19 issue: Issue 1 year: 2024 ident: 92187_CR48 publication-title: J. Artif. Intell. Metaheuristics doi: 10.54216/JAIM.070102 – ident: 92187_CR13 doi: 10.1007/s10462-020-09882-x – ident: 92187_CR18 doi: 10.3389/fenrg.2022.995236 – ident: 92187_CR68 doi: 10.1016/j.eswa.2019.113122 – ident: 92187_CR4 doi: 10.1080/10095020.2019.1612600 – ident: 92187_CR46 doi: 10.3390/math10234421 – ident: 92187_CR30 doi: 10.3390/e23091189 – ident: 92187_CR37 doi: 10.1016/j.asoc.2015.07.005 – ident: 92187_CR3 doi: 10.1016/j.cie.2021.107250 – ident: 92187_CR6 doi: 10.1016/j.knosys.2018.04.025 – ident: 92187_CR57 doi: 10.1016/j.knosys.2020.106553 – ident: 92187_CR11 doi: 10.1007/s00521-019-04368-6 – ident: 92187_CR16 doi: 10.3390/math10122081 – volume: 103 start-page: e40248 issue: 43 year: 2024 ident: 92187_CR63 publication-title: Medicine doi: 10.1097/MD.0000000000040248 – ident: 92187_CR20 doi: 10.1016/j.eswa.2016.02.042 – ident: 92187_CR69 – ident: 92187_CR7 doi: 10.1007/s00521-016-2473-7 – ident: 92187_CR10 doi: 10.1016/j.asoc.2016.01.019 – ident: 92187_CR17 doi: 10.1007/s00366-021-01342-6 – ident: 92187_CR59 doi: 10.1109/ACCESS.2024.3521002 – ident: 92187_CR15 doi: 10.1016/j.asoc.2019.105509 – ident: 92187_CR35 doi: 10.1016/j.cor.2005.11.017 – volume: 73 start-page: 2371 issue: 2 year: 2022 ident: 92187_CR44 publication-title: Computers Mater. Continua doi: 10.32604/cmc.2022.030447 – ident: 92187_CR12 doi: 10.1007/s42979-021-00687-5 – volume: 73 start-page: 5771 issue: 3 year: 2022 ident: 92187_CR47 publication-title: Computers Mater. Continua doi: 10.32604/cmc.2022.032229 – start-page: 241 volume-title: Kusum Deep, Jagdish Chand Bansal, and Kedar Nath Das, Editors, Soft Computing for Problem Solving 2019, Advances in Intelligent Systems and Computing year: 2020 ident: 92187_CR39 – ident: 92187_CR49 doi: 10.54216/JSDGT.030205 – ident: 92187_CR43 doi: 10.3390/pr11051502 – volume: 67 start-page: 3885 issue: 5 year: 2024 ident: 92187_CR62 publication-title: J. Med. Chem. doi: 10.1021/acs.jmedchem.3c02232 – ident: 92187_CR8 doi: 10.1111/exsy.13088 – ident: 92187_CR34 doi: 10.1016/j.compeleceng.2017.09.001 – ident: 92187_CR65 doi: 10.3390/diagnostics14232632 – ident: 92187_CR19 doi: 10.1016/j.ejor.2017.08.040 – volume: 46 start-page: 883 issue: 1 year: 2023 ident: 92187_CR40 publication-title: Comput. Syst. Sci. Eng. doi: 10.32604/csse.2023.034697 – ident: 92187_CR55 doi: 10.1145/2791405.2791549 – ident: 92187_CR26 doi: 10.1109/INTELCIS.2017.8260031 – ident: 92187_CR42 doi: 10.1016/j.energy.2022.124363 – ident: 92187_CR9 doi: 10.1109/IBCAST.2019.8667106 – ident: 92187_CR58 doi: 10.1016/j.knosys.2012.11.005 – ident: 92187_CR2 doi: 10.1016/j.jocs.2017.07.018 – ident: 92187_CR27 doi: 10.1016/j.knosys.2020.105746 – ident: 92187_CR24 doi: 10.1016/j.matcom.2021.08.013 – ident: 92187_CR28 doi: 10.3390/en16031185 – ident: 92187_CR29 doi: 10.1016/j.apm.2021.04.018 – ident: 92187_CR36 doi: 10.1007/s00500-016-2106-1 – ident: 92187_CR1 doi: 10.1007/s10462-020-09860-3 – ident: 92187_CR14 doi: 10.1155/2019/2537689 – volume: 71 start-page: 4989 issue: 3 year: 2022 ident: 92187_CR45 publication-title: Computers Mater. Continua doi: 10.32604/cmc.2022.023884 – volume: 14 start-page: 489 issue: 4 year: 2019 ident: 92187_CR56 publication-title: Int. J. Bus. Intell. Data Min. – volume: 41 start-page: 707 issue: 5 year: 2018 ident: 92187_CR67 publication-title: Biol. Pharm. Bull. doi: 10.1248/bpb.b17-00882 – ident: 92187_CR21 doi: 10.1016/j.asoc.2015.07.023 – ident: 92187_CR31 doi: 10.1007/s12652-018-1031-9 – volume: 7 start-page: 39496 year: 2019 ident: 92187_CR5 publication-title: IEEE Access. doi: 10.1109/ACCESS.2019.2906757 – volume: 4 start-page: 0013 year: 2023 ident: 92187_CR61 publication-title: Cyborg Bionic Syst. doi: 10.34133/cbsystems.0013 – ident: 92187_CR53 doi: 10.1007/s00521-017-2988-6 – ident: 92187_CR38 doi: 10.1016/j.swevo.2012.09.002 – ident: 92187_CR23 doi: 10.1007/BF01608556 – ident: 92187_CR54 doi: 10.1016/j.asoc.2015.10.005 – ident: 92187_CR50 doi: 10.54216/JSDGT.030205 – ident: 92187_CR32 doi: 10.1109/TPAMI.2004.105 – ident: 92187_CR52 doi: 10.1109/IC3IoT.2018.8668174 – ident: 92187_CR64 doi: 10.1109/TNNLS.2022.3165627 – volume: 138 start-page: 106674 year: 2023 ident: 92187_CR66 publication-title: Bioorg. Chem. doi: 10.1016/j.bioorg.2023.106674 – ident: 92187_CR25 doi: 10.1007/s42835-021-00961-9 – ident: 92187_CR51 doi: 10.54216/FinTech-I.020203
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SubjectTerms	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
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Title	Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithm
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