Akademik Veri Yönetim Sistemi
Journal of mathematical sciences and modelling (Online), cilt.8, sa.3, ss.144-152, 2025 (TRDizin)
Parkinson’s disease (PD) is a neurodegenerative disorder that most profoundly affects motor capabilities. Machine learning approaches have emerged as potential tools for early diagnosis, accomplished through analysis of impairments exhibited in handwriting and drawing tests. In contrast with previous studies, in this work, ensemble learning approaches are incorporated in PD detection through handwriting tests for assessments of motor capabilities for the first time. Specifically, a composite ensemble approach is utilized, combining Random Forest and Gradient Boosting Classifiers, with a Voting Classifier added for enhancing model robustness and preventing overfitting, allowing for increased generalization to new cases. In addition, a careful analysis of feature importance is conducted, and it is determined that pressure and tilt variation act as key markers for PD-related impairments in motor capabilities. Earlier studies have focused predominantly on analysis of motion path locations (X, Y coordinates); in contrast, in this work, the dynamics of variation in pressure and tilt, factors relatively less focused upon but with increased diagnostic value, are emphasized. In contrast with conventional studies utilizing static handwriting tests, in this work, velocity variation enters into diagnostics, and analysis is conducted of variation in drawing fluidity and rapid motion variation contributing to classification performance.