Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR
<b>Objective:</b> The cancer death rate has accelerated at an alarming rate, making accurate diagnosis at the primary stages crucial to enhance prognosis. This has deepened the issue of cancer mortality, which is already at an exponential scale. It has been observed that concentration on...
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MDPI AG
2025-03-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/3/291 |
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| author | Bibhuprasad Sahu Amrutanshu Panigrahi Abhilash Pati Manmath Nath Das Prince Jain Ghanashyam Sahoo Haipeng Liu |
| author_facet | Bibhuprasad Sahu Amrutanshu Panigrahi Abhilash Pati Manmath Nath Das Prince Jain Ghanashyam Sahoo Haipeng Liu |
| author_sort | Bibhuprasad Sahu |
| collection | DOAJ |
| description | <b>Objective:</b> The cancer death rate has accelerated at an alarming rate, making accurate diagnosis at the primary stages crucial to enhance prognosis. This has deepened the issue of cancer mortality, which is already at an exponential scale. It has been observed that concentration on datasets drawn from supporting primary sources using machine learning algorithms brings the accuracy expected for cancer diagnosis. <b>Methods:</b> This research presents an innovative cancer classification technique that combines fast minimum redundancy-maximum relevance-based feature selection with Binary Portia Spider Optimization Algorithm to optimize features. The features selected, with the aid of fast mRMR and tested with a range of classifiers, Support Vector Machine, Weighted Support Vector Machine, Extreme Gradient Boosting, Adaptive Boosting, and Random Forest classifier, are tested for comprehensively proofed performance. <b>Results:</b> The classification efficiency of the advanced model is tested on six different cancer datasets that exhibit classification challenges. The empirical analysis confirms that the proposed methodology FmRMR-BPSOA is effective since it reached the highest accuracy of 99.79%. The result is of utmost significance as the proposed model emphasizes the need for alternative and highly efficient greater precision cancer diagnosis. The classification accuracy concludes that the model holds great promise for real-life medical implementations. |
| format | Article |
| id | doaj-art-902674c2c49542e4b8c622dc7000e70c |
| institution | DOAJ |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-902674c2c49542e4b8c622dc7000e70c2025-08-20T02:42:45ZengMDPI AGBioengineering2306-53542025-03-0112329110.3390/bioengineering12030291Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMRBibhuprasad Sahu0Amrutanshu Panigrahi1Abhilash Pati2Manmath Nath Das3Prince Jain4Ghanashyam Sahoo5Haipeng Liu6Department of Information Technology, Vardhaman College of Engineering (Autonomous), Hyderabad 501218, Telangana, IndiaDepartment of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, IndiaDepartment of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, Odisha, IndiaDepartment of Artificial Intelligence & Data Science, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology (VNRVJIET), Hyderabad 500090, Telengana, IndiaDepartment of Mechatronics Engineering, Parul Institute of Technology, Parul University, Vadodara 391760, Gujarat, IndiaDepartment of Computer Science and Engineering, GITA Autonomous College, Bhubaneswar 752054, Odisha, IndiaCentre for Intelligent Healthcare, Coventry University, Coventry CV1 5RW, UK<b>Objective:</b> The cancer death rate has accelerated at an alarming rate, making accurate diagnosis at the primary stages crucial to enhance prognosis. This has deepened the issue of cancer mortality, which is already at an exponential scale. It has been observed that concentration on datasets drawn from supporting primary sources using machine learning algorithms brings the accuracy expected for cancer diagnosis. <b>Methods:</b> This research presents an innovative cancer classification technique that combines fast minimum redundancy-maximum relevance-based feature selection with Binary Portia Spider Optimization Algorithm to optimize features. The features selected, with the aid of fast mRMR and tested with a range of classifiers, Support Vector Machine, Weighted Support Vector Machine, Extreme Gradient Boosting, Adaptive Boosting, and Random Forest classifier, are tested for comprehensively proofed performance. <b>Results:</b> The classification efficiency of the advanced model is tested on six different cancer datasets that exhibit classification challenges. The empirical analysis confirms that the proposed methodology FmRMR-BPSOA is effective since it reached the highest accuracy of 99.79%. The result is of utmost significance as the proposed model emphasizes the need for alternative and highly efficient greater precision cancer diagnosis. The classification accuracy concludes that the model holds great promise for real-life medical implementations.https://www.mdpi.com/2306-5354/12/3/291cancer predictionfast mRMRbinary Portia spider optimization (BPSOA)feature selectionweighted SVM |
| spellingShingle | Bibhuprasad Sahu Amrutanshu Panigrahi Abhilash Pati Manmath Nath Das Prince Jain Ghanashyam Sahoo Haipeng Liu Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR Bioengineering cancer prediction fast mRMR binary Portia spider optimization (BPSOA) feature selection weighted SVM |
| title | Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR |
| title_full | Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR |
| title_fullStr | Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR |
| title_full_unstemmed | Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR |
| title_short | Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR |
| title_sort | novel hybrid feature selection using binary portia spider optimization algorithm and fast mrmr |
| topic | cancer prediction fast mRMR binary Portia spider optimization (BPSOA) feature selection weighted SVM |
| url | https://www.mdpi.com/2306-5354/12/3/291 |
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