Détail du document
Identifiant
oai:arXiv.org:2302.00973
Sujet
Statistics - Machine Learning Computer Science - Machine Learnin...Auteur
Wang, Xuechao Huang, Junqing Chatzakou, Marianna Medijainen, Kadri Taba, Pille Toomela, Aaro Nomm, Sven Ruzhansky, MichaelCatégorie
Computer Science
Année
2023
Date de référencement
08/02/2023
Mots clés
disease parkinson learning methods machine modelRésumé
In recent years, deep learning methods have achieved great success in various fields due to their strong performance in practical applications.
In this paper, we present a light-weight neural network for Parkinson's disease diagnostics, in which a series of hand-drawn data are collected to distinguish Parkinson's disease patients from healthy control subjects.
The proposed model consists of a convolution neural network (CNN) cascading to long-short-term memory (LSTM) to adapt the characteristics of collected time-series signals.
To make full use of their advantages, a multilayered LSTM model is firstly used to enrich features which are then concatenated with raw data and fed into a shallow one-dimensional (1D) CNN model for efficient classification.
Experimental results show that the proposed model achieves a high-quality diagnostic result over multiple evaluation metrics with much fewer parameters and operations, outperforming conventional methods such as support vector machine (SVM), random forest (RF), lightgbm (LGB) and CNN-based methods.
Wang, Xuechao,Huang, Junqing,Chatzakou, Marianna,Medijainen, Kadri,Taba, Pille,Toomela, Aaro,Nomm, Sven,Ruzhansky, Michael, 2023, A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics
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