Department of Computer Science, Atma Ram Sanatan Dharma College, University of Delhi, New Delhi, India.
Breast Dis. 2024;43(1):257-270. doi: 10.3233/BD-240018.
Breast Cancer is the leading form of cancer found in women and a major cause of increased mortality rates among them. However, manual diagnosis of the disease is time-consuming and often limited by the availability of screening systems. Thus, there is a pressing need for an automatic diagnosis system that can quickly detect cancer in its early stages. Data mining and machine learning techniques have emerged as valuable tools in developing such a system. In this study we investigated the performance of several machine learning models on the Wisconsin Breast Cancer (original) dataset with a particular emphasis on finding which models perform the best for breast cancer diagnosis. The study also explores the contrast between the proposed ANN methodology and conventional machine learning techniques. The comparison between the methods employed in the current study and those utilized in earlier research on the Wisconsin Breast Cancer dataset is also compared. The findings of this study are in line with those of previous studies which also highlighted the efficacy of SVM, Decision Tree, CART, ANN, and ELM ANN for breast cancer detection. Several classifiers achieved high accuracy, precision and F1 scores for benign and malignant tumours, respectively. It is also found that models with hyperparameter adjustment performed better than those without and boosting methods like as XGBoost, Adaboost, and Gradient Boost consistently performed well across benign and malignant tumours. The study emphasizes the significance of hyperparameter tuning and the efficacy of boosting algorithms in addressing the complexity and nonlinearity of data. Using the Wisconsin Breast Cancer (original) dataset, a detailed summary of the current status of research on breast cancer diagnosis is provided.
乳腺癌是女性中最常见的癌症类型，也是导致女性死亡率上升的主要原因之一。然而，这种疾病的手动诊断既耗时又常常受到筛查系统可用性的限制。因此，迫切需要一种能够快速检测早期癌症的自动诊断系统。数据挖掘和机器学习技术已经成为开发这种系统的有价值的工具。在这项研究中，我们研究了几种机器学习模型在威斯康星州乳腺癌（原始）数据集上的性能，特别关注哪些模型在乳腺癌诊断方面表现最佳。该研究还探讨了所提出的 ANN 方法与传统机器学习技术之间的对比。还比较了当前研究中使用的方法与之前在威斯康星州乳腺癌数据集上进行的研究中使用的方法之间的差异。这项研究的结果与之前的研究结果一致，也强调了 SVM、决策树、CART、ANN 和 ELM-ANN 在乳腺癌检测方面的有效性。对于良性和恶性肿瘤，几种分类器分别实现了高准确性、高精度和 F1 分数。还发现，具有超参数调整的模型比没有超参数调整的模型表现更好，并且像 XGBoost、Adaboost 和 Gradient Boost 这样的提升方法在良性和恶性肿瘤中都表现良好。该研究强调了超参数调整和提升算法在解决数据复杂性和非线性方面的重要性。使用威斯康星州乳腺癌（原始）数据集，对乳腺癌诊断研究的现状进行了详细总结。
