Diagnostic Study for Neurodegenerative Disorders Based on Handwriting Analysis

Subject Areas : BioElectric

Leila Soleimanidoust ¹ , Abdalhossein Rezai ² , Hamideh Barghamadi ³ , Iman Ahanian ⁴

1 - Department of Medical Engineering, South Tehran Branch, Islamic Azad University, Tehran
2 - Department of Electrical Engineering, University of Science and Culture, Tehran
3 - Department of Medical Engineering, South Tehran Branch, Islamic Azad University, Tehran
4 - Department of Electrical Engineering, South Tehran Branch, Islamic Azad University, Tehran

Received: 2023-08-19 Accepted : 2024-01-22 Published : 2024-05-08

Keywords: Feature extraction, Diagnostic study, Handwritten Exam, Neurodegenerative Disorder, Prediction disease,

Abstract :

One of the most frequently acknowledged personal behavioral traits in the biometric system is the handwritten exam. Numerous fields, including e-health, psychological issues, medical diag-nosis, and many more, can benefit from handwriting analysis. In this study, a handwriting-based computer diagnostic method for identifying neurodegenerative disorders is established. The sug-gested computer diagnosis system uses the SFTA feature extraction approach, and the findings are classified using SVM, kNN, and D-Tree algorithms. MATLAB R2021b and the handwritten tests gathered at Botucatu Medical School, So Paulo State University—Brazil—are used to assess the performance of the suggested computer diagnosis method. The best results were related into two models of classifier, Optimizable model of SVM and kNN. The accuracy, sensitivity and specificity are 89.2%, 88.3% and 90.0% for SVM and 89.2%,90.0% and 88.3% for kNN over Meander handwritten exam. These results indicate that the use of SFTA feature extraction method, SVM classification algorithm and handwritten database in the proposed computer diagnosis system give acceptable results.