research-article

Hyper-parameter optimization of deep learning model for prediction of Parkinson’s disease

Authors:

Himanshu Aggarwal, and

Rinkle RaniAuthors Info & Claims

Machine Vision and Applications, Volume 31, Issue 5

https://doi.org/10.1007/s00138-020-01078-1

Published: 09 May 2020 Publication History

Abstract

Neurodegenerative disorder such as Parkinson’s disease (PD) is among the severe health problems in our aging society. It is a neural disorder that affects people socially as well as economically. It occurs due to the failure of the brain’s dopamine-producing cells to produce enough dopamine to enable the motor movement of the body. This disease primarily affects vision, speech, movement problems, and excretion activity, followed by depression, nervousness, sleeping problems, and panic attacks. The onset of Parkinson’s disease is diagnosed with the help of speech disorders, which are the earliest symptoms of it. The essential goal of this paper is to build up a viable clinical decision-making system that helps the doctor in diagnosing the PD influenced patients. In this paper, a specific framework based on grid search optimization is proposed to develop an optimized deep learning Model to predict the early onset of Parkinson’s disease whereby multiple hyperparameters are to be set and tuned for evaluation of the deep learning model. The grid search optimization consists of three main stages, i.e., the optimization of the deep learning model topology, the hyperparameters, and its performance. An evaluation of the proposed approach is done on the speech samples of PD patients and healthy individuals. The results of the approach proposed are finally analyzed, which shows that the fine-tuning of the deep learning model parameters result in the overall test accuracy of 89.23% and the average classification accuracy of 91.69%.

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Cited By

Singh NTripathi P(2024)An ensemble technique to predict Parkinson's disease using machine learning algorithmsSpeech Communication10.1016/j.specom.2024.103067159:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.specom.2024.103067
Caroline El Fiorenza JSellam V(2024)ESDC-LSH: Ensemble Support-Vector Deep Convolutional Based Levy Selfish Herd Optimization for Prediction and Classification of Parkinson’s DiseaseWireless Personal Communications: An International Journal10.1007/s11277-024-11173-5135:3(1861-1883)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s11277-024-11173-5
Goyal PRani R(2023)Comparative Analysis of Machine Learning, Ensemble Learning and Deep Learning Classifiers for Parkinson’s Disease DetectionSN Computer Science10.1007/s42979-023-02368-x5:1Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1007/s42979-023-02368-x
Show More Cited By

Index Terms

Hyper-parameter optimization of deep learning model for prediction of Parkinson’s disease
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Machine Vision and Applications

Machine Vision and Applications Volume 31, Issue 5

Jun 2020

156 pages

ISSN:0932-8092

Issue’s Table of Contents

© Springer-Verlag GmbH Germany, part of Springer Nature 2020.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 09 May 2020

Accepted: 31 March 2020

Revision received: 15 March 2020

Received: 16 October 2019

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Cited By

Singh NTripathi P(2024)An ensemble technique to predict Parkinson's disease using machine learning algorithmsSpeech Communication10.1016/j.specom.2024.103067159:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.specom.2024.103067
Caroline El Fiorenza JSellam V(2024)ESDC-LSH: Ensemble Support-Vector Deep Convolutional Based Levy Selfish Herd Optimization for Prediction and Classification of Parkinson’s DiseaseWireless Personal Communications: An International Journal10.1007/s11277-024-11173-5135:3(1861-1883)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s11277-024-11173-5
Goyal PRani R(2023)Comparative Analysis of Machine Learning, Ensemble Learning and Deep Learning Classifiers for Parkinson’s Disease DetectionSN Computer Science10.1007/s42979-023-02368-x5:1Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1007/s42979-023-02368-x
Dhar J(2022)An adaptive intelligent diagnostic system to predict early stage of parkinson's disease using two-stage dimension reduction with genetically optimized lightgbm algorithmNeural Computing and Applications10.1007/s00521-021-06612-434:6(4567-4593)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s00521-021-06612-4
Wang KHill MKnowles-Barley STikhonov ALitchfield LBare J(2022)Improving Segmentation of Breast Arterial Calcifications from Digital Mammography: Good Annotation is All You NeedComputer Vision – ACCV 2022 Workshops10.1007/978-3-031-27066-6_10(134-150)Online publication date: 4-Dec-2022
https://dl.acm.org/doi/10.1007/978-3-031-27066-6_10

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