ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2018
Sparse feature learning for multi-class Parkinson's disease classification.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm.BMC medical imaging · 2024Article
- Tuning attention based long-short term memory neural networks for Parkinson's disease detection using modified metaheuristics.Scientific reports · 2024Article
- An Improved Method for Diagnosis of Parkinson's Disease using Deep Learning Models Enhanced with Metaheuristic Algorithm.Research square · 2023Article
- An Ensemble of CNN Models for Parkinson's Disease Detection Using DaTscan Images.Diagnostics (Basel, Switzerland) · 2022Article
- Stability Evaluation of Brain Changes in Parkinson's Disease Based on Machine Learning.Frontiers in computational neuroscience · 2021Article
- A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis.Frontiers in neuroscience · 2020Review
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Authors and funding
7 authors.
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Abstract
This paper solves the multi-class classification problem for Parkinson's disease (PD) analysis by a sparse discriminative feature selection framework. Specifically, we propose a framework to construct a least square regression model based on the Fisher's linear discriminant analysis (LDA) and locality preserving projection (LPP). This framework utilizes the global and local information to select the most relevant and discriminative features to boost classification performance. Differing in previous methods for binary classification, we perform a multi-class classification for PD diagnosis. Our proposed method is evaluated on the public available Parkinson's progression markers initiative (PPMI) datasets. Extensive experimental results indicate that our proposed method identifies highly suitable regions for further PD analysis and diagnosis and outperforms state-of-the-art methods.
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