Abstract：In order to realize the visualization of the rules of breast cancer data, a method based on the combination of Lasso and incremental learning, was proposed, using the optimized attribute partial order structure diagram as a tool. Firstly, having the dimensions reduced by using Lasso to select the features of the breast cancer data, and four attributes that gained the largest correlation were selected from nine features. Granulation process was completed under the Gini index, generating the formal context by means of the incremental learning algorithm. Next, the second Lasso process was completed, which made the dimensions reduced from 17 to 3. Meanwhile, a new method processing the rows and columns of the formal context based on the Gini index and the covering theory was proposed to generate the attribute partial order structure diagram to visualize the rules concerned. As there have been seven rules extracted by analyzing the diagram reported in literatures,we compared the proposed classification accuracy of the method with those classical mainstream classifiers. Results showed that the classification precision of our method reached 96.52%, higher than the other five classifiers including Random Forest (94.25%), Adaboost (90.00%), 1NN (91.33%), 3NN (90.67%), and SVM (95.00%). At last, different incremental proportional (10%-90%) data were used to verify the effect of incremental learning algorithm, results showed that the model had been completed when the amount of data reached 30%, and the precision was almost approaching to that of support vector machine, which proved that the proposed method represented an effective means of visualizing the diagnosis rules of breast cancer.
梁怀新, 宋佳霖, 郑存芳, 洪文学. 肿瘤参数属性偏序结构可视化实现乳腺癌诊断[J]. 中国生物医学工程学报, 2018, 37(4): 404-413.
Liang Huaixin, Song Jialin, Zheng Cunfang, Hong Wenxue. Diagnosis of Breast Cancer Based on Tumor Parameters and Visualization of the Attribute Partial Order Structure Diagram. Chinese Journal of Biomedical Engineering, 2018, 37(4): 404-413.
