Breast cancer diagnosis using the fast learning network algorithm - Albadr, Musatafa Abbas Abbood; Ayob, Masri; Tiun, Sabrina; AL-Dhief, Fahad Taha; Arram, Anas; Khalaf, Sura (Frontiers in Oncology, 2023)
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 hasn't been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosing. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multi-class 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 WBCD database. 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 applications-related problems in the healthcare sector.