A New Approach for the Diagnosis of Parkinson’s Disease Using a Similarity Feature Extractor

Abstract

Parkinson’s disease affects millions of people worldwide. Nowadays there are several ways to help diagnose this disease. Among which we can highlight handwriting exams. One of the main contributions of the computational field to help diagnose this disease is the feature extraction of handwriting exams. This paper proposed a similarity extraction approach which was applied to the exam template and the handwritten trace of the patient. The similarity metrics used in this work were: structural similarity, mean squared error and peak signal-to-noise ratio. The proposed approach was evaluated with variations in obtaining the exam template and the handwritten trace generated by the patient. Each of these variations was used together with the Nave Bayes, OPF, and SVM classifiers. In conclusion, the proposed approach demonstrated that it was better than the other approach found in the literature, and is therefore a potential aid in the detection and monitoring of Parkinson’s disease.