PDF(Pubmed)
Abstract：
In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier\'s performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier\'s performance emphasizes the role of feature selection methods.
摘要：
在这项研究中,我们专注于使用胰腺来源的微阵列基因数据来检测糖尿病。使用降维（DR）技术来减少高维微阵列基因数据。像贝塞尔函数这样的DR方法，离散余弦变换(DCT),最小二乘线性回归(LSLR)，并使用人工藻类算法（AAA）。随后，我们应用元启发式算法，如蜻蜓优化算法（DOA）和大象羊群优化算法（EHO）进行特征选择。分类器，如非线性回归(NLR)，线性回归(LR)，高斯混合模型(GMM)期望最大值（EM），贝叶斯线性判别分类器(BLDC)，Logistic回归(LoR)，Softmax判别分类器(SDC)，以及具有三种类型内核的支持向量机(SVM)，线性,多项式,和径向基函数(RBF)，被用来检测糖尿病。分类器的性能是根据精度等参数进行分析的，F1得分,MCC,错误率,FM度量，还有Kappa.如果没有功能选择，SVM(RBF)分类器使用AAADR方法实现了90%的高准确率。使用AAADR方法进行EHO特征选择的SVM（RBF）分类器优于其他分类器，准确率为95.714％。分类器性能精度的提高强调了特征选择方法的作用。
