A bi-objective hybrid optimization algorithm to reduce noise and data dimension in diabetes diagnosis using support vector machines

作者：

Highlights：

• The PIMA Indian Type-2 diabetes dataset is used.

• Pre-processing techniques are combined together to access high-quality data.

• Significant features are found using SVM.

• Four bi-objective meta-heuristics are employed to maximize the accuracy and to minimize the number of selected features.

• The 10-fold cross validation method is used to validate the constructed model.

摘要

•The PIMA Indian Type-2 diabetes dataset is used.•Pre-processing techniques are combined together to access high-quality data.•Significant features are found using SVM.•Four bi-objective meta-heuristics are employed to maximize the accuracy and to minimize the number of selected features.•The 10-fold cross validation method is used to validate the constructed model.

论文关键词：Diabetes diagnosis,Feature selection,Meta-heuristic algorithms,K-means algorithms,Support vector machine

论文评审过程：Received 7 October 2018, Revised 23 January 2019, Accepted 28 February 2019, Available online 28 February 2019, Version of Record 8 March 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.02.037