Classification of healthcare data using genetic fuzzy logic system and wavelets

作者：

Highlights：

• Introduce GSAM model by incorporating genetic algorithm in SAM learning process.

• GSAM learning has lower computational costs and higher efficiency compared to SAM.

• Employ wavelet transformation for feature extraction in high-dimensional datasets.

• This is the first application of fuzzy SAM method in medical diagnosis.

• This is the first combination of wavelets and fuzzy SAM applied in classification.

摘要

•Introduce GSAM model by incorporating genetic algorithm in SAM learning process.•GSAM learning has lower computational costs and higher efficiency compared to SAM.•Employ wavelet transformation for feature extraction in high-dimensional datasets.•This is the first application of fuzzy SAM method in medical diagnosis.•This is the first combination of wavelets and fuzzy SAM applied in classification.

论文关键词：Fuzzy standard additive model,Genetic algorithm,Wavelet transformation,Healthcare data classification,Breast cancer,Heart disease

论文评审过程：Available online 23 October 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.10.027