Breast cancer diagnosis using the fast learning network algorithm
The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1150840/full |
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| author | Musatafa Abbas Abbood Albadr Masri Ayob Sabrina Tiun Fahad Taha AL-Dhief Anas Arram Sura Khalaf |
| author_facet | Musatafa Abbas Abbood Albadr Masri Ayob Sabrina Tiun Fahad Taha AL-Dhief Anas Arram Sura Khalaf |
| author_sort | Musatafa Abbas Abbood Albadr |
| collection | DOAJ |
| description | The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, F-measure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector. |
| first_indexed | 2024-03-13T03:10:44Z |
| format | Article |
| id | doaj.art-55cc288b80964ac1b9c0827d568637c0 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-13T03:10:44Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-55cc288b80964ac1b9c0827d568637c02023-06-26T13:19:31ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-04-011310.3389/fonc.2023.11508401150840Breast cancer diagnosis using the fast learning network algorithmMusatafa Abbas Abbood Albadr0Masri Ayob1Sabrina Tiun2Fahad Taha AL-Dhief3Anas Arram4Sura Khalaf5Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, MalaysiaCenter for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, MalaysiaCenter for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, MalaysiaDepartment of Communication Engineering, School of Electrical Engineering, Universiti Teknologi Malaysia, (UTM), Johor Bahru, Johor, MalaysiaDepartment of Computer Science, Birzeit University, Birzeit, PalestineDepartment of Communication Technology Engineering, College of Information Technology, Imam Ja’afer Al-Sadiq University, Baghdad, IraqThe use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, F-measure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector.https://www.frontiersin.org/articles/10.3389/fonc.2023.1150840/fullbreast cancermachine learning algorithmsdata mining algorithmsfast learning networkWisconsin breast cancer databaseWisconsin Diagnostic Breast Cancer |
| spellingShingle | Musatafa Abbas Abbood Albadr Masri Ayob Sabrina Tiun Fahad Taha AL-Dhief Anas Arram Sura Khalaf Breast cancer diagnosis using the fast learning network algorithm Frontiers in Oncology breast cancer machine learning algorithms data mining algorithms fast learning network Wisconsin breast cancer database Wisconsin Diagnostic Breast Cancer |
| title | Breast cancer diagnosis using the fast learning network algorithm |
| title_full | Breast cancer diagnosis using the fast learning network algorithm |
| title_fullStr | Breast cancer diagnosis using the fast learning network algorithm |
| title_full_unstemmed | Breast cancer diagnosis using the fast learning network algorithm |
| title_short | Breast cancer diagnosis using the fast learning network algorithm |
| title_sort | breast cancer diagnosis using the fast learning network algorithm |
| topic | breast cancer machine learning algorithms data mining algorithms fast learning network Wisconsin breast cancer database Wisconsin Diagnostic Breast Cancer |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1150840/full |
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