Parkinson Disease Detection Based on In-air Dynamics Feature Extraction and Selection Using Machine Learning
Gespeichert in:
| Veröffentlicht in: | arXiv.org (Dec 19, 2024), p. n/a |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | , , , |
| Veröffentlicht: |
Cornell University Library, arXiv.org
|
| Schlagworte: | |
| Online-Zugang: | Citation/Abstract Full text outside of ProQuest |
| Tags: |
Keine Tags, Fügen Sie das erste Tag hinzu!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3149106416 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3149106416 | ||
| 045 | 0 | |b d20241219 | |
| 100 | 1 | |a Shin, Jungpil | |
| 245 | 1 | |a Parkinson Disease Detection Based on In-air Dynamics Feature Extraction and Selection Using Machine Learning | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 19, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Parkinson's disease (PD) is a progressive neurological disorder that impairs movement control, leading to symptoms such as tremors, stiffness, and bradykinesia. Many researchers analyzing handwriting data for PD detection typically rely on computing statistical features over the entirety of the handwriting task. While this method can capture broad patterns, it has several limitations, including a lack of focus on dynamic change, oversimplified feature representation, lack of directional information, and missing micro-movements or subtle variations. Consequently, these systems face challenges in achieving good performance accuracy, robustness, and sensitivity. To overcome this problem, we proposed an optimized PD detection methodology that incorporates newly developed dynamic kinematic features and machine learning (ML)-based techniques to capture movement dynamics during handwriting tasks. In the procedure, we first extracted 65 newly developed kinematic features from the first and last 10% phases of the handwriting task rather than using the entire task. Alongside this, we also reused 23 existing kinematic features, resulting in a comprehensive new feature set. Next, we enhanced the kinematic features by applying statistical formulas to compute hierarchical features from the handwriting data. This approach allows us to capture subtle movement variations that distinguish PD patients from healthy controls. To further optimize the feature set, we applied the Sequential Forward Floating Selection method to select the most relevant features, reducing dimensionality and computational complexity. Finally, we employed an ML-based approach based on ensemble voting across top-performing tasks, achieving an impressive 96.99\% accuracy on task-wise classification and 99.98% accuracy on task ensembles, surpassing the existing state-of-the-art model by 2% for the PaHaW dataset. | |
| 653 | |a Feature extraction | ||
| 653 | |a Accuracy | ||
| 653 | |a Machine learning | ||
| 653 | |a Kinematics | ||
| 653 | |a Neurological diseases | ||
| 653 | |a Handwriting | ||
| 653 | |a Motion perception | ||
| 653 | |a Disease control | ||
| 653 | |a Task complexity | ||
| 653 | |a Parkinson's disease | ||
| 700 | 1 | |a Abu Saleh Musa Miah | |
| 700 | 1 | |a Hirooka, Koki | |
| 700 | 1 | |a Md Al Mehedi Hasan | |
| 700 | 1 | |a Maniruzzaman, Md | |
| 773 | 0 | |t arXiv.org |g (Dec 19, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3149106416/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.17849 |