Reshan Mana Saleh Al, Amin Samina, Zeb Muhammad Ali, Sulaiman Adel, Alshahrani Hani, Azar Ahmad Taher, Shaikh Asadullah
Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia.
Institute of Computing, Kohat University of Science and Technology, Kohat 26000, Pakistan.
Life (Basel). 2023 Oct 21;13(10):2093. doi: 10.3390/life13102093.
Breast cancer (BC) is the most common cancer among women, making it essential to have an accurate and dependable system for diagnosing benign or malignant tumors. It is essential to detect this cancer early in order to inform subsequent treatments. Currently, fine needle aspiration (FNA) cytology and machine learning (ML) models can be used to detect and diagnose this cancer more accurately. Consequently, an effective and dependable approach needs to be developed to enhance the clinical capacity to diagnose this illness. This study aims to detect and divide BC into two categories using the Wisconsin Diagnostic Breast Cancer (WDBC) benchmark feature set and to select the fewest features to attain the highest accuracy. To this end, this study explores automated BC prediction using multi-model features and ensemble machine learning (EML) techniques. To achieve this, we propose an advanced ensemble technique, which incorporates voting, bagging, stacking, and boosting as combination techniques for the classifier in the proposed EML methods to distinguish benign breast tumors from malignant cancers. In the feature extraction process, we suggest a recursive feature elimination technique to find the most important features of the WDBC that are pertinent to BC detection and classification. Furthermore, we conducted cross-validation experiments, and the comparative results demonstrated that our method can effectively enhance classification performance and attain the highest value in six evaluation metrics, including precision, sensitivity, area under the curve (AUC), specificity, accuracy, and F1-score. Overall, the stacking model achieved the best average accuracy, at 99.89%, and its sensitivity, specificity, F1-score, precision, and AUC/ROC were 1.00%, 0.999%, 1.00%, 1.00%, and 1.00%, respectively, thus generating excellent results. The findings of this study can be used to establish a reliable clinical detection system, enabling experts to make more precise and operative decisions in the future. Additionally, the proposed technology might be used to detect a variety of cancers.
乳腺癌（BC）是女性中最常见的癌症，因此拥有一个准确可靠的系统来诊断良性或恶性肿瘤至关重要。早期发现这种癌症对于后续治疗至关重要。目前，细针穿刺（FNA）细胞学和机器学习（ML）模型可用于更准确地检测和诊断这种癌症。因此，需要开发一种有效且可靠的方法来提高诊断这种疾病的临床能力。本研究旨在使用威斯康星诊断乳腺癌（WDBC）基准特征集检测BC并将其分为两类，并选择最少的特征以获得最高的准确率。为此，本研究探索了使用多模型特征和集成机器学习（EML）技术进行自动化BC预测。为实现这一目标，我们提出了一种先进的集成技术，该技术将投票、装袋、堆叠和增强作为所提出的EML方法中分类器的组合技术，以区分良性乳腺肿瘤和恶性癌症。在特征提取过程中，我们提出了一种递归特征消除技术，以找到与BC检测和分类相关的WDBC最重要特征。此外，我们进行了交叉验证实验，比较结果表明，我们的方法可以有效提高分类性能，并在包括精度、灵敏度、曲线下面积（AUC）、特异性、准确率和F1分数在内的六个评估指标中获得最高值。总体而言，堆叠模型的平均准确率最高，为99.89%，其灵敏度、特异性、F1分数、精度和AUC/ROC分别为1.00%、0.999%、1.00%、1.00%和1.00%，从而产生了优异的结果。本研究的结果可用于建立一个可靠的临床检测系统，使专家能够在未来做出更精确和有效的决策。此外，所提出的技术可能用于检测多种癌症。
