Extreme Learning machine algorithm for breast Cancer diagnosis - kadhim ajlan, Iman; Murad, Hayder; Salim, A. A.; fadhil bin yousif, Ahmad (Multimedia tools and applications, 2025)
Recently, significant attention has been given to using machine learning (ML) and data mining algorithms for diagnosing breast cancer (BC). However, many of these efforts still require improvement. Either they need proper statistical evaluation, or they use inadequate assessment metrics or both. In this context, the extreme learning machine (ELM) algorithm, known for its effectiveness, emerges as a promising approach for data classification. Therefore, this study proposes the ELM algorithm and statistically evaluates its performance in diagnosing BC. The ELM algorithm offers several advantages: it can eliminate overfitting, address binary and multi-class classification issues, and exhibit performance comparable to a kernel-based support vector machine with a neural network structure. To evaluate the ELM algorithm’s performance, two BC datasets, namely the Wisconsin breast cancer database (WBCD) and the Wisconsin diagnostic breast cancer (WDBC) were used. The experimental results demonstrated the excellent performance of the ELM algorithm. Using the WBCD dataset, the ELM algorithm achieved an average accuracy of 92.06%, precision of 80.25%, recall of 96.60%, F-measure of 87.56%, G-mean of 87.99%, MCC of 82.67%, and specificity of 90.27%. Similarly, using the WDBC dataset, it achieved an average accuracy of 94.52%, precision of 92.28%, recall of 93.09%, F-measure of 92.66%, G-mean of 92.67%, MCC of 88.32%, and specificity of 95.42%. These results highlight the ELM algorithm’s reliability as a classifier for diagnosing BC and its potential for addressing healthcare-related problems in other applications.