Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson’s disease
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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-41314-y |
| _version_ | 1797452772055973888 |
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| author | Umesh Kumar Lilhore Surjeet Dalal Neetu Faujdar Martin Margala Prasun Chakrabarti Tulika Chakrabarti Sarita Simaiya Pawan Kumar Pugazhenthan Thangaraju Hemasri Velmurugan |
| author_facet | Umesh Kumar Lilhore Surjeet Dalal Neetu Faujdar Martin Margala Prasun Chakrabarti Tulika Chakrabarti Sarita Simaiya Pawan Kumar Pugazhenthan Thangaraju Hemasri Velmurugan |
| author_sort | Umesh Kumar Lilhore |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T15:13:30Z |
| format | Article |
| id | doaj.art-3ed6546957684ef8b38545e5d08ca456 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:13:30Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-3ed6546957684ef8b38545e5d08ca4562023-11-26T13:11:53ZengNature PortfolioScientific Reports2045-23222023-09-0113112210.1038/s41598-023-41314-yHybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson’s diseaseUmesh Kumar Lilhore0Surjeet Dalal1Neetu Faujdar2Martin Margala3Prasun Chakrabarti4Tulika Chakrabarti5Sarita Simaiya6Pawan Kumar7Pugazhenthan Thangaraju8Hemasri Velmurugan9Department of Computer Science and Engineering, Chandigarh UniversityAmity School of Engineering and Technology, Amity University HaryanaDepartment of Computer Engineering and Application, GLA UniversitySchool of Computing and Informatics, University of Louisiana at LafayetteDepartment of Computer Science and Engineering, Sir Padampat Singhania UniversitySir Padampat Singhania UniversityDepartment of Computer Science and Engineering, Chandigarh UniversityDepartment of Computer Science and Engineering, Chandigarh UniversityDepartment of Pharmacology, All India Institute of Medical SciencesDepartment of Pharmacology, All India Institute of Medical SciencesAbstract 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.https://doi.org/10.1038/s41598-023-41314-y |
| spellingShingle | Umesh Kumar Lilhore Surjeet Dalal Neetu Faujdar Martin Margala Prasun Chakrabarti Tulika Chakrabarti Sarita Simaiya Pawan Kumar Pugazhenthan Thangaraju Hemasri Velmurugan Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson’s disease Scientific Reports |
| title | Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson’s disease |
| title_full | Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson’s disease |
| title_fullStr | Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson’s disease |
| title_full_unstemmed | Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson’s disease |
| title_short | Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson’s disease |
| title_sort | hybrid cnn lstm model with efficient hyperparameter tuning for prediction of parkinson s disease |
| url | https://doi.org/10.1038/s41598-023-41314-y |
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