A novel intelligent classification model for breast cancer diagnosis
A novel intelligent classification model for breast cancer diagnosis
复制标题
一种新型乳腺癌智能分类模型
DOI：
10.1016/j.ipm.2018.10.014
复制
发表时间：
2019-05-01
影响因子：
8.6
通讯作者：
Liu, Gui-Qiu
中科院分区：
文献类型：
--
作者：
Liu, Na;Qi, Er-Shi;Liu, Gui-Qiu
乳腺癌是全世界妇女死亡的主要原因之一。乳腺癌的早期准确诊断可以保证患者的长期生存。然而，传统的分类算法通常只以最大化分类精度为目标，而没有考虑不同类别之间的误分类代价。此外，与遗漏癌症病例（假阴性）相关的成本远远高于错误标记良性病例（假阳性）的成本。为了克服这一缺点，进一步提高乳腺癌诊断的分类准确率，本文提出了一种新的乳腺癌智能诊断方法，该方法采用信息增益定向模拟退火遗传算法包装器（IGSAGAW）进行特征选择，在此过程中，我们根据IG算法对特征进行排序，利用代价敏感支持向量机（CSSVM）学习算法提取前m个最优特征。本文提出的特征选择方法不仅可以在一定程度上降低SAGASW算法的复杂度，有效地提取最优特征子集，而且可以获得最大的分类精度和最小的误分类代价。在威斯康星州原始乳腺癌（WBC）和威斯康星州诊断性乳腺癌（WDBC）乳腺癌数据集上测试了该方法的有效性，结果表明，该混合算法优于其他比较方法。本研究的主要目的是将我们的研究成果应用于真实的临床诊断系统中，从而帮助临床医师在未来做出正确有效的决策，而且我们提出的方法也可以应用于其他疾病的诊断。
Breast cancer is one of the leading causes of death among women worldwide. Accurate and early detection of breast cancer can ensure long-term surviving for the patients. However, traditional classification algorithms usually aim only to maximize the classification accuracy, failing to take into consideration the misclassification costs between different categories. Furthermore, the costs associated with missing a cancer case (false negative) are dearly much higher than those of mislabeling a benign one (false positive). To overcome this drawback and further improving the classification accuracy of the breast cancer diagnosis, in this work, a novel breast cancer intelligent diagnosis approach has been proposed, which employed information gain directed simulated annealing genetic algorithm wrapper (IGSAGAW) for feature selection, in this process, we performs the ranking of features according to IG algorithm, and extracting the top m optimal feature utilized the cost sensitive support vector machine (CSSVM) learning algorithm. Our proposed feature selection approach which can not only help to reduce the complexity of SAGASW algorithm and effectively extracting the optimal feature subset to a certain extent, but it can also obtain the maximum classification accuracy and minimum misclassification cost. The efficacy of our proposed approach is tested on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer data sets, and the results demonstrate that our proposed hybrid algorithm outperforms other comparison methods. The main objective of this study was to apply our research in real clinical diagnostic system and thereby assist clinical physicians in making correct and effective decisions in the future, Moreover our proposed method could also be applied to other illness diagnosis.