Abstract：
This paper introduces a simple strategy for diagnosing disease, which is called improved gray wolf optimization (IGWO) and ensemble classification. The proposed strategy consists of two sequential phases, which are; (i) Feature Selection Phase (FSP) and (ii) Ensemble Classification Phase (ECP). During the former, the most effective features for diagnosing disease are selected, while during the latter, the actual diagnosis takes place depending on voting of five different classifiers. The main contribution of this paper is a suggested modification for the traditional Gray Wolf Optimization (GWO), which is called Improved Gray Wolf Optimization (IGWO). As an optimization technique, the proposed IGWO is employed in the FSP for selecting the effective features. For evaluating, IGWO has been implemented using recent feature selection techniques as well as the proposed method. To accomplish the classification phase; ensemble classification has been used which uses several classification techniques such as; Naïve Bayes (NB), Support Vector Machines (SVM), Deep Neural Network (DNN), Decision Tree (DT), and K-Nearest Neighbors (KNN). Ensemble classification integrate several classifiers for improving prediction performance. Experimental results have shown that employing IGWO promotes the performance of the diagnosing strategy of different diseases in terms of precision, recall, and accuracy.
摘要：
本文介绍了一种简单的疾病诊断策略,这被称为改进的灰狼优化(IGWO)和集成分类。拟议的战略包括两个连续的阶段，它们是:(i)特征选择阶段(FSP)和(ii)集成分类阶段(ECP)。在前者,选择诊断疾病的最有效特征，而在后者中,实际诊断取决于五个不同分类器的投票。本文的主要贡献是对传统灰狼优化(GWO)的建议修改，这就是所谓的改进灰狼优化（IGWO）。作为一种优化技术，在FSP中采用所提出的IGWO来选择有效特征。为了评估，IGWO已经使用最近的特征选择技术以及所提出的方法来实现。为了完成分类阶段；已经使用了集成分类，它使用了几种分类技术，例如；朴素贝叶斯(NB)，支持向量机(SVM)深度神经网络(DNN)决策树(DT)K-近邻（KNN）集成分类集成了多个分类器，以提高预测性能。实验结果表明，采用IGWO促进了不同疾病的诊断策略在精度方面的表现，召回，和准确性。
