Fusing voice and query data for non-invasive detection of laryngeal disorders

作者：

Highlights：

• Voice and query data are explored for the task of laryngeal disorders detection.

• Decision-level fusion by complete-case analysis is compared to imputation strategies.

• Query data outperform voice, fusion after iterative model-based imputation – the best.

• Human readable rules were extracted from the query data using affinity analysis.

摘要

•Voice and query data are explored for the task of laryngeal disorders detection.•Decision-level fusion by complete-case analysis is compared to imputation strategies.•Query data outperform voice, fusion after iterative model-based imputation – the best.•Human readable rules were extracted from the query data using affinity analysis.

论文关键词：Ensemble methods,Random forest,Variable importance,Imputation,Affinity analysis,Voice pathology detection

论文评审过程：Received 23 March 2015, Revised 5 June 2015, Accepted 1 July 2015, Available online 6 July 2015, Version of Record 29 August 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.07.001