Cancer Center, Foshan Fuxing Chancheng Hospital, Foshan, 528000, Guangdong, China.
Department of Computer Science, Khayyam University, Mashhad, Iran.
J Cancer Res Clin Oncol. 2023 Nov;149(16):14519-14534. doi: 10.1007/s00432-023-05238-4. Epub 2023 Aug 12.
Advances in technology have led to the emergence of computerized diagnostic systems as intelligent medical assistants. Machine learning approaches cannot replace professional humans, but they can change the treatment of diseases such as cancer and be used as medical assistants.
Breast cancer treatment can be very effective, especially when the disease is detected in the early stages. Feature selection and classification are common data mining techniques in machine learning that can provide breast cancer diagnosis with high speed, low cost and high precision.
This paper proposes a new intelligent approach using an integrated filter-evolutionary search-based feature selection and an optimized ensemble classifier for breast cancer diagnosis. The selected features mainly relate to the viable solution as the selected features are successfully used in the breast cancer disease classification process. The proposed feature selection method selects the most informative features from the original feature set by integrating adaptive thresholder information gain-based feature selection and evolutionary gravity-search-based feature selection. Meanwhile, classification model is done by proposing a new intelligent multi-layer perceptron neural network-based ensemble classifier.
The simulation results show that the proposed method provides better performance compared to the state-of-the-art algorithms in terms of various criteria such as accuracy, sensitivity and specificity. Specifically, the proposed method achieves an average accuracy of 99.42% on WBCD, WDBC and WPBC datasets from Wisconsin database with only 56.7% of features.
Systems based on intelligent medical assistants configured with machine learning approaches are an important step toward helping doctors to detect breast cancer early.
技术的进步催生了计算机化的诊断系统作为智能医疗助手的出现。机器学习方法无法替代专业的人类，但可以改变癌症等疾病的治疗方式，并作为医疗助手使用。
乳腺癌的治疗可以非常有效，尤其是在疾病早期发现时。特征选择和分类是机器学习中常见的数据挖掘技术，可以为乳腺癌诊断提供高速、低成本和高精度。
本文提出了一种新的智能方法，使用集成的过滤-进化搜索的特征选择和优化的集成分类器进行乳腺癌诊断。选择的特征主要与可行的解决方案有关，因为所选特征成功地用于乳腺癌疾病分类过程。所提出的特征选择方法通过集成自适应门限信息增益特征选择和基于进化重力搜索的特征选择，从原始特征集中选择最具信息量的特征。同时，通过提出一种新的基于智能多层感知器神经网络的集成分类器来进行分类模型。
模拟结果表明，与最先进的算法相比，该方法在准确性、敏感性和特异性等各种标准下都具有更好的性能。具体来说，该方法在威斯康星州数据库的 WBCD、WDBC 和 WPBC 数据集上实现了 99.42%的平均准确率，仅使用了 56.7%的特征。
配置机器学习方法的智能医疗助手系统是帮助医生早期发现乳腺癌的重要一步。
