title: Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.
abstract: BACKGROUND
  The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined.
  
  
  METHODS
  In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation.
  
  
  RESULTS
  Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms.
  
  
  CONCLUSIONS
  This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.
authors.affiliation: Institute of Surgery Research, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
authors.forename: He-Hua
authors.initials: HH
authors.lastname: Zhang
authors.affiliation: College of Communication Engineering, Chongqing University, Chongqing, 400044, China.
authors.forename: Liuyang
authors.initials: L
authors.lastname: Yang
authors.affiliation: College of Communication Engineering, Chongqing University, Chongqing, 400044, China.
authors.forename: Yuchuan
authors.initials: Y
authors.lastname: Liu
authors.affiliation: College of Communication Engineering, Chongqing University, Chongqing, 400044, China.
authors.forename: Pin
authors.initials: P
authors.lastname: Wang
authors.affiliation: Institute of Surgery Research, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Yin
authors.affiliation: College of Communication Engineering, Chongqing University, Chongqing, 400044, China. yongmingli@cqu.edu.cn.|Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038, China. yongmingli@cqu.edu.cn.
authors.forename: Yongming
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038, China.
authors.forename: Mingguo
authors.initials: M
authors.lastname: Qiu
authors.affiliation: College of Communication Engineering, Chongqing University, Chongqing, 400044, China.
authors.forename: Xueru
authors.initials: X
authors.lastname: Zhu
authors.affiliation: College of Communication Engineering, Chongqing University, Chongqing, 400044, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Yan
mesh_terms: D006801:Humans; D016571:Neural Networks, Computer; D010300:Parkinson Disease / Q000145:classification*; D013060:Speech
keywords: Classification of Parkinson disease; Decorrelated neural network ensembles (DNNE); Ensemble learning; Multi-edit-nearest-neighbor algorithm (MENN); Optimal selection of speech samples; Random forest (RF)