Diagnostic Study for Parkinson'S Disease Based on Handwriting Analysis Using Computational Intelligence Techniques.

Parkinson's disease (PD) is a long-term disease that mainly influences the central nervous system and thus affects movement, such as inability to move rigidity, and tremors. So, analysis of patients' movements under control, especially handwriting, is a helpful way to diagnose Parkinson's disease. Diagnosis, as the first step in medical practice, is critical to clinical decision-making. This paper uses multiple computational intelligence classification techniques such as Decision Tree, Naive Bayes, Support Vector Machine, and Random Forest to investigate the existence of the PD. Also, Convolutional Neural Network (CNN) and the Best First (BF) strategy are used as feature extractors. These techniques are applied over both Meander and Spiral data and some selected traits derived from the patient's handwriting during the handwritten exam. The available HandPD dataset has been used with both its images and selected attributes. The CNN is used for the feature extraction process across the images of the used dataset. Whereas, the BF search strategy is used to extract features based on the changes between the handwritten trace and the exam template features combined with instances resampling. Compared with other well-known diagnostic systems, the proposed one has the highest recognition rate. [ABSTRACT FROM AUTHOR]