ArticleScientific reports2023
Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson's disease.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Applications of Artificial Intelligence in Neurological Voice Disorders.World journal of otorhinolaryngology - head and neck surgery · 2025Review
- Improving Sepsis Mortality Prediction With Machine Learning Using Full Region Synthetic Sampling Approach.Health science reports · 2025Article
- HybridBranchNetV2: Towards reliable artificial intelligence in image classification using reinforcement learning.PloS one · 2025Article
- Intelligence model on sequence-based prediction of PPI using AISSO deep concept with hyperparameter tuning process.Scientific reports · 2024Article
- PARKINSON'S DISEASE CLASSIFICATION USING CONTRASTIVE GRAPH CROSS-VIEW LEARNING WITH MULTIMODAL FUSION OF SPECT IMAGES AND CLINICAL FEATURES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Automated segmentation and classification of supraspinatus fatty infiltration in shoulder magnetic resonance image using a convolutional neural network.Frontiers in medicine · 2024Article
Corrections and comments
- Correction · 2024-04-23Error in Text · Updated to Retraction · · See also: https://pubpeer.com/publications/BE2E334302A3B028B84D49A35C44B1
- Retraction · 2024-11-07Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Duplication of Text · Duplication of/in Image · Error in Data · Error in Text · Euphemisms for Plagiarism · Investigation by Third Party · Objections by Author(s) · Original Data and/or Images not Provided and/or not Available · Unreliable Results and/or Conclusions · Upgrade/Update of Prior Notice(s) · · See also: https://pubpeer.com/publications/BE2E334302A3B028B84D49A35C44B1
- Erratum issued
- Retracted
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The patients' vocal Parkinson's disease (PD) changes could be identified early on, allowing for management before physically incapacitating symptoms appear. In this work, static as well as dynamic speech characteristics that are relevant to PD identification are examined. Speech changes or communication issues are among the challenges that Parkinson's individuals may encounter. As a result, avoiding the potential consequences of speech difficulties brought on by the condition depends on getting the appropriate diagnosis early. PD patients' speech signals change significantly from those of healthy individuals. This research presents a hybrid model utilizing improved speech signals with dynamic feature breakdown using CNN and LSTM. The proposed hybrid model employs a new, pre-trained CNN with LSTM to recognize PD in linguistic features utilizing Mel-spectrograms derived from normalized voice signal and dynamic mode decomposition. The proposed Hybrid model works in various phases, which include Noise removal, extraction of Mel-spectrograms, feature extraction using pre-trained CNN model ResNet-50, and the final stage is applied for classification. An experimental analysis was performed using the PC-GITA disease dataset. The proposed hybrid model is compared with traditional NN and well-known machine learning-based CART and SVM & XGBoost models. The accuracy level achieved in Neural Network, CART, SVM, and XGBoost models is 72.69%, 84.21%, 73.51%, and 90.81%. The results show that under these four machine approaches of tenfold cross-validation and dataset splitting without samples overlapping one individual, the proposed hybrid model achieves an accuracy of 93.51%, significantly outperforming traditional ML models utilizing static features in detecting Parkinson's disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.