Parkinson’s Disease Prediction: An Attention-Based Multimodal Fusion Framework Using Handwriting and Clinical Data
Background: Neurodegenerative diseases (NGD) encompass a range of progressive neurological conditions, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), characterised by the gradual deterioration of neuronal structure and function. This degeneration manifests as cognitive decline, movem...
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MDPI AG
2024-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/1/4 |
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| author | Sabrina Benredjem Tahar Mekhaznia Abdulghafor Rawad Sherzod Turaev Akram Bennour Bourmatte Sofiane Abdulaziz Aborujilah Mohamed Al Sarem |
| author_facet | Sabrina Benredjem Tahar Mekhaznia Abdulghafor Rawad Sherzod Turaev Akram Bennour Bourmatte Sofiane Abdulaziz Aborujilah Mohamed Al Sarem |
| author_sort | Sabrina Benredjem |
| collection | DOAJ |
| description | Background: Neurodegenerative diseases (NGD) encompass a range of progressive neurological conditions, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), characterised by the gradual deterioration of neuronal structure and function. This degeneration manifests as cognitive decline, movement impairment, and dementia. Our focus in this investigation is on PD, a neurodegenerative disorder characterized by the loss of dopamine-producing neurons in the brain, leading to motor disturbances. Early detection of PD is paramount for enhancing quality of life through timely intervention and tailored treatment. However, the subtle nature of initial symptoms, like slow movements, tremors, muscle rigidity, and psychological changes, often reduce daily task performance and complicate early diagnosis. Method: To assist medical professionals in timely diagnosis of PD, we introduce a cutting-edge Multimodal Diagnosis framework (PMMD). Based on deep learning techniques, the PMMD framework integrates imaging, handwriting, drawing, and clinical data to accurately detect PD. Notably, it incorporates cross-modal attention, a methodology previously unexplored within the area, which facilitates the modelling of interactions between different data modalities. Results: The proposed method exhibited an accuracy of 96% on the independent tests set. Comparative analysis against state-of-the-art models, along with an in-depth exploration of attention mechanisms, highlights the efficacy of PMMD in PD classification. Conclusions: The obtained results highlight exciting new prospects for the use of handwriting as a biomarker, along with other information, for optimal model performance. PMMD’s success in integrating diverse data sources through cross-modal attention underscores its potential as a robust diagnostic decision support tool for accurately diagnosing PD. |
| format | Article |
| id | doaj-art-2fd978e50bfc4b909eb5d51964307164 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-2fd978e50bfc4b909eb5d519643071642025-01-10T13:16:25ZengMDPI AGDiagnostics2075-44182024-12-01151410.3390/diagnostics15010004Parkinson’s Disease Prediction: An Attention-Based Multimodal Fusion Framework Using Handwriting and Clinical DataSabrina Benredjem0Tahar Mekhaznia1Abdulghafor Rawad2Sherzod Turaev3Akram Bennour4Bourmatte Sofiane5Abdulaziz Aborujilah6Mohamed Al Sarem7Laboratory of Mathematics, Informatics and Systems (LAMIS), Echahid Cheikh Larbi Tebessi University, Tebessa 12002, AlgeriaLaboratory of Mathematics, Informatics and Systems (LAMIS), Echahid Cheikh Larbi Tebessi University, Tebessa 12002, AlgeriaFaculty of Computer Studies (FCS), Arab Open University–Oman, P.O. Box 1596, Muscat 130, OmanDepartment of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab EmiratesLaboratory of Mathematics, Informatics and Systems (LAMIS), Echahid Cheikh Larbi Tebessi University, Tebessa 12002, AlgeriaIndependent Researcher, Ain Mlila 04002, AlgeriaDepartment of Management Information System, College of Commerce & Business Administration, Dhofar University, Salalah 211, OmanDepartment of Information Technology, Aylol University College, Yarim 547, YemenBackground: Neurodegenerative diseases (NGD) encompass a range of progressive neurological conditions, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), characterised by the gradual deterioration of neuronal structure and function. This degeneration manifests as cognitive decline, movement impairment, and dementia. Our focus in this investigation is on PD, a neurodegenerative disorder characterized by the loss of dopamine-producing neurons in the brain, leading to motor disturbances. Early detection of PD is paramount for enhancing quality of life through timely intervention and tailored treatment. However, the subtle nature of initial symptoms, like slow movements, tremors, muscle rigidity, and psychological changes, often reduce daily task performance and complicate early diagnosis. Method: To assist medical professionals in timely diagnosis of PD, we introduce a cutting-edge Multimodal Diagnosis framework (PMMD). Based on deep learning techniques, the PMMD framework integrates imaging, handwriting, drawing, and clinical data to accurately detect PD. Notably, it incorporates cross-modal attention, a methodology previously unexplored within the area, which facilitates the modelling of interactions between different data modalities. Results: The proposed method exhibited an accuracy of 96% on the independent tests set. Comparative analysis against state-of-the-art models, along with an in-depth exploration of attention mechanisms, highlights the efficacy of PMMD in PD classification. Conclusions: The obtained results highlight exciting new prospects for the use of handwriting as a biomarker, along with other information, for optimal model performance. PMMD’s success in integrating diverse data sources through cross-modal attention underscores its potential as a robust diagnostic decision support tool for accurately diagnosing PD.https://www.mdpi.com/2075-4418/15/1/4Parkinson’s diseasemultimodal fusionattention mechanismfeatures fusionartificial neural network |
| spellingShingle | Sabrina Benredjem Tahar Mekhaznia Abdulghafor Rawad Sherzod Turaev Akram Bennour Bourmatte Sofiane Abdulaziz Aborujilah Mohamed Al Sarem Parkinson’s Disease Prediction: An Attention-Based Multimodal Fusion Framework Using Handwriting and Clinical Data Diagnostics Parkinson’s disease multimodal fusion attention mechanism features fusion artificial neural network |
| title | Parkinson’s Disease Prediction: An Attention-Based Multimodal Fusion Framework Using Handwriting and Clinical Data |
| title_full | Parkinson’s Disease Prediction: An Attention-Based Multimodal Fusion Framework Using Handwriting and Clinical Data |
| title_fullStr | Parkinson’s Disease Prediction: An Attention-Based Multimodal Fusion Framework Using Handwriting and Clinical Data |
| title_full_unstemmed | Parkinson’s Disease Prediction: An Attention-Based Multimodal Fusion Framework Using Handwriting and Clinical Data |
| title_short | Parkinson’s Disease Prediction: An Attention-Based Multimodal Fusion Framework Using Handwriting and Clinical Data |
| title_sort | parkinson s disease prediction an attention based multimodal fusion framework using handwriting and clinical data |
| topic | Parkinson’s disease multimodal fusion attention mechanism features fusion artificial neural network |
| url | https://www.mdpi.com/2075-4418/15/1/4 |
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