Progress prediction of Parkinson's disease based on graph wavelet transform and attention weighted random forest

作者：

Highlights：

• A novel model is proposed to predict the severity of Parkinson's disease.

• More efficient frequency features are obtained by graph wavelet transform.

• The attentional mechanism weights decision tree models in the random forest.

• We achieve better prediction performance compared to the state-of-the-art methods.

摘要

•A novel model is proposed to predict the severity of Parkinson's disease.•More efficient frequency features are obtained by graph wavelet transform.•The attentional mechanism weights decision tree models in the random forest.•We achieve better prediction performance compared to the state-of-the-art methods.

论文关键词：Parkinson's disease,Progress prediction,Graph wavelet transform,Attentional mechanism,Random forest

论文评审过程：Received 7 September 2021, Revised 30 March 2022, Accepted 29 April 2022, Available online 6 May 2022, Version of Record 10 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117483