Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Detection of Parkinson's Disease Using Spiral Drawings and Machine Learning Approaches. |
| Συγγραφείς: |
Ramírez, Axell Albano Gutiérrez, Paredes, Antonio Alarcón, Márquez, Cornelio Yáñez |
| Πηγή: |
Journal of Image & Graphics (United Kingdom); 2026, Vol. 14 Issue 1, p58-64, 7p |
| Θεματικοί όροι: |
Parkinson's disease, Machine learning, Diagnostic services, Random forest algorithms, Support vector machines, Neurodegeneration, Graphology |
| Περίληψη: |
This paper presents the design of a pre-diagnostic system for Parkinson's Disease (PD) based on the analysis of hand-drawn spiral patterns using machine learning techniques. Parkinson's disease is a progressive neurodegenerative disorder whose early motor manifestations--such as micrographia and tremors--can be reflected in fine motor tasks like handwriting. Although handwriting disturbances are not part of the core diagnostic criteria, they are frequently observed in early stages and are recognized by the Movement Disorder Society as supportive markers. In this study, grayscale spiral drawings are preprocessed and binarized using Otsu's thresholding method. From each image, 100 equidistant pixel coordinates are extracted where the trace is present, forming structured feature vectors. These coordinates are then used to train and evaluate several machine learning classifiers, including Random Forest, k-Nearest Neighbors, and Support Vector Machines. The proposed method prioritizes simplicity, explainability, and computational efficiency, offering a lightweight yet effective tool for early Parkinson's detection. Experimental results demonstrate the model's potential as a clinically relevant and accessible diagnostic support system. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |