Age-Aware Multimodal Web-Based Framework for Parkinson's Disease Detection Using Handwriting and Voice Analysis

Abstract: Parkinson's Disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide, characterized by motor impairments including tremors, rigidity, and postural instability. Early and accurate diagnosis is critical for improving patient outcomes, yet traditional clinical diagnosis often lacks objective, quantitative measures. This paper proposes an Age-Aware Multimodal Web-Based Framework for automated Parkinson's Disease detection by integrating three complementary modalities: voice acoustic features, handwriting drawing analysis (spiral and wave patterns), and clinical biomarkers (Jitter, Shimmer, HNR). Three age groups — Gen Z (18–26 years), Adults (28–59 years), and Seniors (60+ years) — are incorporated to capture demographic diversity. Feature extraction employs Mel- Frequency Cepstral Coefficients (MFCC) for voice signals and Histogram of Oriented Gradients (HOG) for handwriting images. Classification models including Support Vector Machine (SVM) and Random Forest (RF) are trained per modality and fused via a weighted ensemble (Voice: 40%, Spiral: 30%, Wave: 30%). The clinical SVM achieves the highest accuracy of 86.36%, spiral RF achieves 85.71%, and wave RF achieves 76.19%. The complete system is deployed as a Flask-based web application enabling real-time multimodal patient assessment. Experiments over a total of 392 samples (107 clinical, 81 voice, 102 spiral images, 102 wave images) demonstrate the effectiveness of the proposed age- aware multimodal approach for non-invasive PD screening.

Keywords: Parkinson's Disease Detection, Multimodal Classification, MFCC, HOG, SVM, Random Forest, Ensemble Learning, Flask Web Application, Age-Aware Framework.