College of Computer Science and Technology, Jilin University, Changchun, Jilin, China.
J Med Syst. 2012 Jun;36(3):1953-63. doi: 10.1007/s10916-011-9655-8. Epub 2011 Feb 1.
In this paper, we present a three-stage expert system based on a hybrid support vector machines (SVM) approach to diagnose thyroid disease. Focusing on feature selection, the first stage aims at constructing diverse feature subsets with different discriminative capability. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the designed SVM classifier for training an optimal predictor model whose parameters are optimized by particle swarm optimization (PSO). Finally, the obtained optimal SVM model proceeds to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. The effectiveness of the proposed expert system (FS-PSO-SVM) has been rigorously evaluated against the thyroid disease dataset, which is commonly used among researchers who use machine learning methods for thyroid disease diagnosis. The proposed system has been compared with two other related methods including the SVM based on the Grid search technique (Grid-SVM) and the SVM based on Grid search and principle component analysis (PCA-Grid-SVM) in terms of their classification accuracy. Experimental results demonstrate that FS-PSO-SVM significantly outperforms the other ones. In addition, Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far by 10-fold cross-validation (CV) method, with the mean accuracy of 97.49% and with the maximum accuracy of 98.59%. Promisingly, the proposed FS-PSO-SVM expert system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.
在本文中，我们提出了一种基于混合支持向量机（SVM）方法的三阶段专家系统，用于诊断甲状腺疾病。 本研究聚焦于特征选择，第一阶段旨在构建具有不同判别能力的多样化特征子集。 从特征选择切换到模型构建，在第二阶段，将获得的特征子集输入到设计的 SVM 分类器中，以训练最优预测器模型，该模型的参数由粒子群优化（PSO）优化。 最后，使用最具判别力的特征子集和最优参数，获得的最优 SVM 模型执行甲状腺疾病诊断任务。 已经使用常用的甲状腺疾病数据集对所提出的专家系统（FS-PSO-SVM）的有效性进行了严格评估，该数据集被研究人员用于甲状腺疾病诊断的机器学习方法。 该系统已经与另外两种相关方法进行了比较，包括基于网格搜索技术的 SVM（Grid-SVM）和基于网格搜索和主成分分析的 SVM（PCA-Grid-SVM），其比较的基准是分类准确性。 实验结果表明，FS-PSO-SVM 明显优于其他方法。 此外，与之前研究中的现有方法相比，该系统通过 10 倍交叉验证（CV）方法实现了迄今为止报告的最高分类准确性，平均准确率为 97.49%，最高准确率为 98.59%。 有希望的是，所提出的 FS-PSO-SVM 专家系统可能成为一种新的候选强大工具，用于诊断甲状腺疾病，具有出色的性能。
