School of Information Science and Engineering, Shandong Normal University, Jinan City, China; Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan City, China.
School of Information Science and Engineering, Shandong Normal University, Jinan City, China; Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan City, China.
J Biomed Inform. 2019 Apr;92:103124. doi: 10.1016/j.jbi.2019.103124. Epub 2019 Feb 20.
Microarray technique is a prevalent method for the classification and prediction of colorectal cancer (CRC). Nevertheless, microarray data suffers from the curse of dimensionality when selecting feature genes of the disease based on imbalance samples, thus causing low prediction accuracy. Hence, it is of vital significance to build proper models that can avoid the above problems and predict the CRC more accurately. In this paper, we use an ensemble model to classify samples into healthy and CRC groups and improve prediction performance. The proposed model is composed of three functional modules. The first module mainly performs the function of removing redundant genes. The main feature genes are selected using minimum redundancy maximum relevance (mRMR) method to reduce the dimensionality of features thereby increasing the prediction results. The second module aims to solve the problem caused by imbalanced data using hybrid sampling algorithm RUSBoost. The third module focuses on the classification algorithm optimization. We use mixed kernel function (MKF) based support vector machine (SVM) model to classify an unknown sample into healthy individuals and CRC patients, and then, the Whale Optimization Algorithm (WOA) is applied to find most optimal parameters of the proposed MKF-SVM. The final results show that the proposed model achieves higher G-means than other comparable models. The conclusion comes to show that RUSBoost wrapping WOA + MKF-SVM model can be applied to improve the predictive performance of colorectal cancer based on the imbalanced data.
微阵列技术是一种常用于分类和预测结直肠癌（CRC）的方法。然而，基于不平衡样本选择疾病特征基因时，微阵列数据会受到维度诅咒的影响，从而导致预测精度低。因此，构建适当的模型以避免上述问题并更准确地预测 CRC 具有重要意义。在本文中，我们使用集成模型将样本分类为健康组和 CRC 组，并提高预测性能。所提出的模型由三个功能模块组成。第一个模块主要执行去除冗余基因的功能。使用最小冗余最大相关性（mRMR）方法选择主要特征基因，以减少特征的维数，从而提高预测结果。第二个模块旨在使用混合采样算法 RUSBoost 解决不平衡数据引起的问题。第三个模块侧重于分类算法优化。我们使用基于混合核函数（MKF）的支持向量机（SVM）模型将未知样本分类为健康个体和 CRC 患者，然后应用鲸鱼优化算法（WOA）找到所提出的 MKF-SVM 的最优化参数。最终结果表明，所提出的模型比其他可比模型具有更高的 G-均值。结论表明，RUSBoost 包装的 WOA+MKF-SVM 模型可应用于提高基于不平衡数据的结直肠癌预测性能。
