Faculty of Computers and Information, Mansoura University, Mansoura, Egypt.
School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, India.
Sci Rep. 2019 Jul 3;9(1):9583. doi: 10.1038/s41598-019-46074-2.
At present times, healthcare systems are updated with advanced capabilities like machine learning (ML), data mining and artificial intelligence to offer human with more intelligent and expert healthcare services. This paper introduces an intelligent prediction and classification system for healthcare, namely Density based Feature Selection (DFS) with Ant Colony based Optimization (D-ACO) algorithm for chronic kidney disease (CKD). The proposed intelligent system eliminates irrelevant or redundant features by DFS in prior to the ACO based classifier construction. The proposed D-ACO framework three phases namely preprocessing, Feature Selection (FS) and classification. Furthermore, the D-ACO algorithm is tested using benchmark CKD dataset and the performance are investigated based on different evaluation factors. Comparing the D-ACO algorithm with existing methods, the presented intelligent system outperformed the other methodologies with a significant improvisation in classification accuracy using fewer features.
目前，医疗保健系统已经更新了先进的功能，如机器学习 (ML)、数据挖掘和人工智能，为人类提供更智能和专业的医疗保健服务。本文介绍了一种用于医疗保健的智能预测和分类系统，即基于密度的特征选择 (DFS) 和基于蚁群的优化 (D-ACO) 算法的慢性肾脏病 (CKD)。所提出的智能系统通过 DFS 在基于 ACO 的分类器构建之前消除不相关或冗余的特征。所提出的 D-ACO 框架分为三个阶段，即预处理、特征选择 (FS) 和分类。此外，使用基准 CKD 数据集对 D-ACO 算法进行了测试，并基于不同的评估因素研究了性能。与现有方法相比，所提出的智能系统在使用较少特征的情况下，在分类准确性方面表现优于其他方法，显著提高了分类准确性。
