Single and Multi-modal Analysis for Parkinson’s Disease to Detect Its Underlying Factors
Abstract Parkinson’s disease (PD) is a neurological condition characterized by the disruption of both motor and non-motor functions. Given the absence of a definitive diagnostic method, it is crucial to uncover its root causes. Consequently, individuals displaying symptoms of Parkinson’s disease can...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Islam, Nusrat [verfasserIn] Turza, Md Shaiful Alam [verfasserIn] Fahim, Shazzadul Islam [verfasserIn] Rahman, Rashedur M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2024 |
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| Übergeordnetes Werk: |
Enthalten in: Human-centric intelligent systems - Springer Netherlands, 2021, 4(2024), 2 vom: 16. Apr., Seite 316-334 |
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| Übergeordnetes Werk: |
volume:4 ; year:2024 ; number:2 ; day:16 ; month:04 ; pages:316-334 |
| Links: |
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| DOI / URN: |
10.1007/s44230-024-00069-z |
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SPR05613651X |
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| 520 | |a Abstract Parkinson’s disease (PD) is a neurological condition characterized by the disruption of both motor and non-motor functions. Given the absence of a definitive diagnostic method, it is crucial to uncover its root causes. Consequently, individuals displaying symptoms of Parkinson’s disease can promptly receive treatment and comprehensive care. To address this, our study aims to develop an AI-powered system capable of detecting Parkinson’s disease and subsequently evaluating the primary factors influencing its development. We collected 12 distinct datasets from the well-known PPMI database, covering various medical assessments such as motor abilities, olfaction, cognition, sleep patterns, and depressive symptoms. Subsequently, we refined this raw data using advanced search techniques to tailor it to our model’s requirements. Moreover, we introduced a novel labeling approach known as the majority voting algorithm. Following data preparation, we conducted Single and Multi-Modality analyses, focusing on single-treatment approaches and integrating multiple treatments for a comprehensive therapeutic strategy. To analyze these both, we employed five distinct Machine Learning algorithms. Notably, the Support Vector Machine (linear) emerged as the top performer, reaching an accuracy of 100% in both single and multimodality analysis. Furthermore, we employed four tree-based models for feature selection, with the Gradient Boosted Decision Tree excels in identifying the most significant features. Finally, we employed an Artificial Neural Network utilizing these key features, achieving the highest accuracy of 91.41%. | ||
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10.1007/s44230-024-00069-z doi (DE-627)SPR05613651X (SPR)s44230-024-00069-z-e DE-627 ger DE-627 rakwb eng Islam, Nusrat verfasserin aut Single and Multi-modal Analysis for Parkinson’s Disease to Detect Its Underlying Factors 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Parkinson’s disease (PD) is a neurological condition characterized by the disruption of both motor and non-motor functions. Given the absence of a definitive diagnostic method, it is crucial to uncover its root causes. Consequently, individuals displaying symptoms of Parkinson’s disease can promptly receive treatment and comprehensive care. To address this, our study aims to develop an AI-powered system capable of detecting Parkinson’s disease and subsequently evaluating the primary factors influencing its development. We collected 12 distinct datasets from the well-known PPMI database, covering various medical assessments such as motor abilities, olfaction, cognition, sleep patterns, and depressive symptoms. Subsequently, we refined this raw data using advanced search techniques to tailor it to our model’s requirements. Moreover, we introduced a novel labeling approach known as the majority voting algorithm. Following data preparation, we conducted Single and Multi-Modality analyses, focusing on single-treatment approaches and integrating multiple treatments for a comprehensive therapeutic strategy. To analyze these both, we employed five distinct Machine Learning algorithms. Notably, the Support Vector Machine (linear) emerged as the top performer, reaching an accuracy of 100% in both single and multimodality analysis. Furthermore, we employed four tree-based models for feature selection, with the Gradient Boosted Decision Tree excels in identifying the most significant features. Finally, we employed an Artificial Neural Network utilizing these key features, achieving the highest accuracy of 91.41%. Artificial intelligence (dpeaa)DE-He213 Parkinson’s disease (dpeaa)DE-He213 Majority voting technique (dpeaa)DE-He213 Gradient boosted decision tree (dpeaa)DE-He213 Turza, Md Shaiful Alam verfasserin aut Fahim, Shazzadul Islam verfasserin aut Rahman, Rashedur M. verfasserin (orcid)0000-0002-4514-6279 aut Enthalten in Human-centric intelligent systems Springer Netherlands, 2021 4(2024), 2 vom: 16. Apr., Seite 316-334 (DE-627)1807086755 (DE-600)3121986-X 2667-1336 nnns volume:4 year:2024 number:2 day:16 month:04 pages:316-334 https://dx.doi.org/10.1007/s44230-024-00069-z X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2024 2 16 04 316-334 |
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10.1007/s44230-024-00069-z doi (DE-627)SPR05613651X (SPR)s44230-024-00069-z-e DE-627 ger DE-627 rakwb eng Islam, Nusrat verfasserin aut Single and Multi-modal Analysis for Parkinson’s Disease to Detect Its Underlying Factors 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Parkinson’s disease (PD) is a neurological condition characterized by the disruption of both motor and non-motor functions. Given the absence of a definitive diagnostic method, it is crucial to uncover its root causes. Consequently, individuals displaying symptoms of Parkinson’s disease can promptly receive treatment and comprehensive care. To address this, our study aims to develop an AI-powered system capable of detecting Parkinson’s disease and subsequently evaluating the primary factors influencing its development. We collected 12 distinct datasets from the well-known PPMI database, covering various medical assessments such as motor abilities, olfaction, cognition, sleep patterns, and depressive symptoms. Subsequently, we refined this raw data using advanced search techniques to tailor it to our model’s requirements. Moreover, we introduced a novel labeling approach known as the majority voting algorithm. Following data preparation, we conducted Single and Multi-Modality analyses, focusing on single-treatment approaches and integrating multiple treatments for a comprehensive therapeutic strategy. To analyze these both, we employed five distinct Machine Learning algorithms. Notably, the Support Vector Machine (linear) emerged as the top performer, reaching an accuracy of 100% in both single and multimodality analysis. Furthermore, we employed four tree-based models for feature selection, with the Gradient Boosted Decision Tree excels in identifying the most significant features. Finally, we employed an Artificial Neural Network utilizing these key features, achieving the highest accuracy of 91.41%. Artificial intelligence (dpeaa)DE-He213 Parkinson’s disease (dpeaa)DE-He213 Majority voting technique (dpeaa)DE-He213 Gradient boosted decision tree (dpeaa)DE-He213 Turza, Md Shaiful Alam verfasserin aut Fahim, Shazzadul Islam verfasserin aut Rahman, Rashedur M. verfasserin (orcid)0000-0002-4514-6279 aut Enthalten in Human-centric intelligent systems Springer Netherlands, 2021 4(2024), 2 vom: 16. Apr., Seite 316-334 (DE-627)1807086755 (DE-600)3121986-X 2667-1336 nnns volume:4 year:2024 number:2 day:16 month:04 pages:316-334 https://dx.doi.org/10.1007/s44230-024-00069-z X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2024 2 16 04 316-334 |
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10.1007/s44230-024-00069-z doi (DE-627)SPR05613651X (SPR)s44230-024-00069-z-e DE-627 ger DE-627 rakwb eng Islam, Nusrat verfasserin aut Single and Multi-modal Analysis for Parkinson’s Disease to Detect Its Underlying Factors 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Parkinson’s disease (PD) is a neurological condition characterized by the disruption of both motor and non-motor functions. Given the absence of a definitive diagnostic method, it is crucial to uncover its root causes. Consequently, individuals displaying symptoms of Parkinson’s disease can promptly receive treatment and comprehensive care. To address this, our study aims to develop an AI-powered system capable of detecting Parkinson’s disease and subsequently evaluating the primary factors influencing its development. We collected 12 distinct datasets from the well-known PPMI database, covering various medical assessments such as motor abilities, olfaction, cognition, sleep patterns, and depressive symptoms. Subsequently, we refined this raw data using advanced search techniques to tailor it to our model’s requirements. Moreover, we introduced a novel labeling approach known as the majority voting algorithm. Following data preparation, we conducted Single and Multi-Modality analyses, focusing on single-treatment approaches and integrating multiple treatments for a comprehensive therapeutic strategy. To analyze these both, we employed five distinct Machine Learning algorithms. Notably, the Support Vector Machine (linear) emerged as the top performer, reaching an accuracy of 100% in both single and multimodality analysis. Furthermore, we employed four tree-based models for feature selection, with the Gradient Boosted Decision Tree excels in identifying the most significant features. Finally, we employed an Artificial Neural Network utilizing these key features, achieving the highest accuracy of 91.41%. Artificial intelligence (dpeaa)DE-He213 Parkinson’s disease (dpeaa)DE-He213 Majority voting technique (dpeaa)DE-He213 Gradient boosted decision tree (dpeaa)DE-He213 Turza, Md Shaiful Alam verfasserin aut Fahim, Shazzadul Islam verfasserin aut Rahman, Rashedur M. verfasserin (orcid)0000-0002-4514-6279 aut Enthalten in Human-centric intelligent systems Springer Netherlands, 2021 4(2024), 2 vom: 16. Apr., Seite 316-334 (DE-627)1807086755 (DE-600)3121986-X 2667-1336 nnns volume:4 year:2024 number:2 day:16 month:04 pages:316-334 https://dx.doi.org/10.1007/s44230-024-00069-z X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2024 2 16 04 316-334 |
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10.1007/s44230-024-00069-z doi (DE-627)SPR05613651X (SPR)s44230-024-00069-z-e DE-627 ger DE-627 rakwb eng Islam, Nusrat verfasserin aut Single and Multi-modal Analysis for Parkinson’s Disease to Detect Its Underlying Factors 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Parkinson’s disease (PD) is a neurological condition characterized by the disruption of both motor and non-motor functions. Given the absence of a definitive diagnostic method, it is crucial to uncover its root causes. Consequently, individuals displaying symptoms of Parkinson’s disease can promptly receive treatment and comprehensive care. To address this, our study aims to develop an AI-powered system capable of detecting Parkinson’s disease and subsequently evaluating the primary factors influencing its development. We collected 12 distinct datasets from the well-known PPMI database, covering various medical assessments such as motor abilities, olfaction, cognition, sleep patterns, and depressive symptoms. Subsequently, we refined this raw data using advanced search techniques to tailor it to our model’s requirements. Moreover, we introduced a novel labeling approach known as the majority voting algorithm. Following data preparation, we conducted Single and Multi-Modality analyses, focusing on single-treatment approaches and integrating multiple treatments for a comprehensive therapeutic strategy. To analyze these both, we employed five distinct Machine Learning algorithms. Notably, the Support Vector Machine (linear) emerged as the top performer, reaching an accuracy of 100% in both single and multimodality analysis. Furthermore, we employed four tree-based models for feature selection, with the Gradient Boosted Decision Tree excels in identifying the most significant features. Finally, we employed an Artificial Neural Network utilizing these key features, achieving the highest accuracy of 91.41%. Artificial intelligence (dpeaa)DE-He213 Parkinson’s disease (dpeaa)DE-He213 Majority voting technique (dpeaa)DE-He213 Gradient boosted decision tree (dpeaa)DE-He213 Turza, Md Shaiful Alam verfasserin aut Fahim, Shazzadul Islam verfasserin aut Rahman, Rashedur M. verfasserin (orcid)0000-0002-4514-6279 aut Enthalten in Human-centric intelligent systems Springer Netherlands, 2021 4(2024), 2 vom: 16. Apr., Seite 316-334 (DE-627)1807086755 (DE-600)3121986-X 2667-1336 nnns volume:4 year:2024 number:2 day:16 month:04 pages:316-334 https://dx.doi.org/10.1007/s44230-024-00069-z X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2024 2 16 04 316-334 |
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10.1007/s44230-024-00069-z doi (DE-627)SPR05613651X (SPR)s44230-024-00069-z-e DE-627 ger DE-627 rakwb eng Islam, Nusrat verfasserin aut Single and Multi-modal Analysis for Parkinson’s Disease to Detect Its Underlying Factors 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Parkinson’s disease (PD) is a neurological condition characterized by the disruption of both motor and non-motor functions. Given the absence of a definitive diagnostic method, it is crucial to uncover its root causes. Consequently, individuals displaying symptoms of Parkinson’s disease can promptly receive treatment and comprehensive care. To address this, our study aims to develop an AI-powered system capable of detecting Parkinson’s disease and subsequently evaluating the primary factors influencing its development. We collected 12 distinct datasets from the well-known PPMI database, covering various medical assessments such as motor abilities, olfaction, cognition, sleep patterns, and depressive symptoms. Subsequently, we refined this raw data using advanced search techniques to tailor it to our model’s requirements. Moreover, we introduced a novel labeling approach known as the majority voting algorithm. Following data preparation, we conducted Single and Multi-Modality analyses, focusing on single-treatment approaches and integrating multiple treatments for a comprehensive therapeutic strategy. To analyze these both, we employed five distinct Machine Learning algorithms. Notably, the Support Vector Machine (linear) emerged as the top performer, reaching an accuracy of 100% in both single and multimodality analysis. Furthermore, we employed four tree-based models for feature selection, with the Gradient Boosted Decision Tree excels in identifying the most significant features. Finally, we employed an Artificial Neural Network utilizing these key features, achieving the highest accuracy of 91.41%. Artificial intelligence (dpeaa)DE-He213 Parkinson’s disease (dpeaa)DE-He213 Majority voting technique (dpeaa)DE-He213 Gradient boosted decision tree (dpeaa)DE-He213 Turza, Md Shaiful Alam verfasserin aut Fahim, Shazzadul Islam verfasserin aut Rahman, Rashedur M. verfasserin (orcid)0000-0002-4514-6279 aut Enthalten in Human-centric intelligent systems Springer Netherlands, 2021 4(2024), 2 vom: 16. Apr., Seite 316-334 (DE-627)1807086755 (DE-600)3121986-X 2667-1336 nnns volume:4 year:2024 number:2 day:16 month:04 pages:316-334 https://dx.doi.org/10.1007/s44230-024-00069-z X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2024 2 16 04 316-334 |
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Abstract Parkinson’s disease (PD) is a neurological condition characterized by the disruption of both motor and non-motor functions. Given the absence of a definitive diagnostic method, it is crucial to uncover its root causes. Consequently, individuals displaying symptoms of Parkinson’s disease can promptly receive treatment and comprehensive care. To address this, our study aims to develop an AI-powered system capable of detecting Parkinson’s disease and subsequently evaluating the primary factors influencing its development. We collected 12 distinct datasets from the well-known PPMI database, covering various medical assessments such as motor abilities, olfaction, cognition, sleep patterns, and depressive symptoms. Subsequently, we refined this raw data using advanced search techniques to tailor it to our model’s requirements. Moreover, we introduced a novel labeling approach known as the majority voting algorithm. Following data preparation, we conducted Single and Multi-Modality analyses, focusing on single-treatment approaches and integrating multiple treatments for a comprehensive therapeutic strategy. To analyze these both, we employed five distinct Machine Learning algorithms. Notably, the Support Vector Machine (linear) emerged as the top performer, reaching an accuracy of 100% in both single and multimodality analysis. Furthermore, we employed four tree-based models for feature selection, with the Gradient Boosted Decision Tree excels in identifying the most significant features. Finally, we employed an Artificial Neural Network utilizing these key features, achieving the highest accuracy of 91.41%. © The Author(s) 2024 |
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Abstract Parkinson’s disease (PD) is a neurological condition characterized by the disruption of both motor and non-motor functions. Given the absence of a definitive diagnostic method, it is crucial to uncover its root causes. Consequently, individuals displaying symptoms of Parkinson’s disease can promptly receive treatment and comprehensive care. To address this, our study aims to develop an AI-powered system capable of detecting Parkinson’s disease and subsequently evaluating the primary factors influencing its development. We collected 12 distinct datasets from the well-known PPMI database, covering various medical assessments such as motor abilities, olfaction, cognition, sleep patterns, and depressive symptoms. Subsequently, we refined this raw data using advanced search techniques to tailor it to our model’s requirements. Moreover, we introduced a novel labeling approach known as the majority voting algorithm. Following data preparation, we conducted Single and Multi-Modality analyses, focusing on single-treatment approaches and integrating multiple treatments for a comprehensive therapeutic strategy. To analyze these both, we employed five distinct Machine Learning algorithms. Notably, the Support Vector Machine (linear) emerged as the top performer, reaching an accuracy of 100% in both single and multimodality analysis. Furthermore, we employed four tree-based models for feature selection, with the Gradient Boosted Decision Tree excels in identifying the most significant features. Finally, we employed an Artificial Neural Network utilizing these key features, achieving the highest accuracy of 91.41%. © The Author(s) 2024 |
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Abstract Parkinson’s disease (PD) is a neurological condition characterized by the disruption of both motor and non-motor functions. Given the absence of a definitive diagnostic method, it is crucial to uncover its root causes. Consequently, individuals displaying symptoms of Parkinson’s disease can promptly receive treatment and comprehensive care. To address this, our study aims to develop an AI-powered system capable of detecting Parkinson’s disease and subsequently evaluating the primary factors influencing its development. We collected 12 distinct datasets from the well-known PPMI database, covering various medical assessments such as motor abilities, olfaction, cognition, sleep patterns, and depressive symptoms. Subsequently, we refined this raw data using advanced search techniques to tailor it to our model’s requirements. Moreover, we introduced a novel labeling approach known as the majority voting algorithm. Following data preparation, we conducted Single and Multi-Modality analyses, focusing on single-treatment approaches and integrating multiple treatments for a comprehensive therapeutic strategy. To analyze these both, we employed five distinct Machine Learning algorithms. Notably, the Support Vector Machine (linear) emerged as the top performer, reaching an accuracy of 100% in both single and multimodality analysis. Furthermore, we employed four tree-based models for feature selection, with the Gradient Boosted Decision Tree excels in identifying the most significant features. Finally, we employed an Artificial Neural Network utilizing these key features, achieving the highest accuracy of 91.41%. © The Author(s) 2024 |
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