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首页> 外文期刊>Expert systems with applications >Prediction model building and feature selection with support vector machines in breast cancer diagnosis
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Prediction model building and feature selection with support vector machines in breast cancer diagnosis

机译：支持向量机在乳腺癌诊断中的预测模型建立和特征选择

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Breast cancer is a serious problem for the young women of Taiwan. Some medical researches have proved that DNA viruses are one of the high-risk factors closely related to human cancers. Five DNA viruses are studied in this research: specific types of HSV-1 (herpes simplex virus type 1), EBV (Epstein-Barr virus), CMV (cytomegalovirus), HPV (human papillomavirus), and HHV-8 (human herpesvirus-8). The purposes of this study are to obtain the bioinformatics about breast tumor and DNA viruses, and to build an accurate diagnosis model about breast cancer and fibroadenoma. Research efforts have reported with increasing confirmation that the support vector machine (SVM) has a greater accurate diagnosis ability. Therefore, this study constructs a hybrid SVM-based strategy with feature selection to render a diagnosis between the breast cancer and fibroadenoma and to find the important risk factor for breast cancer. The results show that {HSV-1, HHV-8} or {HSV-1, HHV-8, CMV} are the most important features and that the diagnosis model achieved high classification accuracy, at 86% of average overall hit rate. A Linear discriminate analysis (LDA) diagnosis model is also constructed in this study. The LDA model shows that {HSV-1, HHV-8, EBV} or {HSV-1, HHV-8} are significant factors which are similar to that of the SVM-based classifier. However, the classificatory accuracy of the SVM-based classifier is slightly better than that of LDA in the negative hit ratio, positive hit ratio, and overall hit ratio.
机译：乳腺癌对台湾的年轻女性来说是一个严重的问题。一些医学研究证明，DNA病毒是与人类癌症密切相关的高危因素之一。本研究中研究了五种DNA病毒：特定类型的HSV-1（单纯疱疹病毒1型），EBV（EB病毒），巨细胞病毒（巨细胞病毒），HPV（人乳头瘤病毒）和HHV-8（人疱疹病毒- 8）。这项研究的目的是获得有关乳腺癌和DNA病毒的生物信息学，并建立有关乳腺癌和纤维腺瘤的准确诊断模型。越来越多的研究报告表明，支持向量机（SVM）具有更高的准确诊断能力。因此，本研究构建了一种基于SVM的混合策略，具有特征选择功能，可在乳腺癌和纤维腺瘤之间进行诊断，并找到乳腺癌的重要危险因素。结果表明，{HSV-1，HHV-8}或{HSV-1，HHV-8，CMV}是最重要的特征，并且该诊断模型实现了较高的分类精度，平均总命中率达86％。这项研究还建立了线性判别分析（LDA）诊断模型。 LDA模型显示{HSV-1，HHV-8，EBV}或{HSV-1，HHV-8}是与基于SVM的分类器相似的重要因素。但是，基于SVM的分类器的分类准确性在负命中率，正命中率和总命中率方面比LDA略好。

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