A novel sample and feature dependent ensemble approach for Parkinson’s disease detection

作者：Liaqat Ali, Chinmay Chakraborty, Zhiquan He, Wenming Cao, Yakubu Imrana, Joel J. P. C. Rodrigues · 发表于：Neural Computing and Applications · 年份：2022 · DOI：10.1007/s00521-022-07046-2 · 被引用次数：78 · 研究领域：Voice and Speech Disorders、Speech Recognition and Synthesis、Music and Audio Processing

Abstract Parkinson’s disease (PD) is a neurological disease that has been reported to have affected most people worldwide. Recent research pointed out that about 90% of PD patients possess voice disorders. Motivated by this fact, many researchers proposed methods based on multiple types of speech data for PD prediction. However, these methods either face the problem of low rate of accuracy or lack generalization. To develop an approach that will be free of these issues, in this paper we propose a novel ensemble approach. These paper contributions are two folds. First, investigating feature selection integration with deep neural network (DNN) and validating its effectiveness by comparing its performance with conventional DNN and other similar integrated systems. Second, development of a novel ensemble model namely EOFSC (Ensemble model with Optimal Features and Sample Dependant Base Classifiers) that exploits the findings of recently published studies. Recent research pointed out that for different types of voice data, different optimal models are obtained which are sensitive to different types of samples and subsets of features. In this paper, we further consolidate the findings by utilizing the proposed integrated system and propose the development of EOFSC. For multiple types of vowel phonations, multiple base classifiers are obtained which are sensitive to different subsets of features. These features and sample-dependent base classifiers are integrated, and the proposed E...