Breast cancer diagnosis using the fast learning network algorithm

Abstract

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.

Enhanced PRIM recognition using PRI sound and deep learning techniques

“…The statistical findings for all experiments of the proposed DCNN-ELM techniques are presented in Table 6 . Eqs 21 – 23 are utilized for the computation of the μ, RMSE, and STD [ 78 , 80 ].…”

“…These statistical measures were used to evaluate the performance of the proposed DVNN-ELM technique in diagnosing PRIM recognition. The three assessments mentioned are widely recognized as the most prevalent statistical evaluation measures [78][79][80]. The mean quantifies the proximity of the classifier's overall performance across multiple runs to the best answer.…”

“…In contrast, the RMSE quantifies the concentration of the results from various runs around the perfect solution. The STD quantifies the extent to which the outcomes of multiple trials deviate from the average [78][79][80].…”

Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization

“…BO and GS methods were used for optimizing ML classifiers, both SVM and MLP obtained 96.52% accuracy. Albadr et al [16] addressed a fast learning network (FLN) for BC diagnosis that obtained 98.83%, 98.44%, and 99.05% accuracy, precision, and specificity, respectively. The performance of several hidden node numbers such as 25, 50, 75, 100, 125, 150, 175, and 200 of FLN were compared, where FLN with 25 hidden nodes got the highest accuracy.…”

IoT-Based Framework for COVID-19 Detection Using Machine Learning Techniques

“…Furthermore, ML techniques have been used in many medical applications such as COVID-19 detection [18]; lung cancer detection [19]; voice pathology classification [20]; breast cancer detection [21]; and diabetes disease detection [22,23]. One of the most dangerous illnesses facing the world recently is COVID-19 [24].…”