Detection Of Parkinson’s Disease Through Analysis Of Image And Speech Data
Inayathulla Mohammed, Shainaz Abdul, Pradeep Kumar Reddy Tadipatri, Vishnuvardhan Boya, Teja Chenna, Nithin Kumar Reddy Modiam
Parkinson’s disease (PD) is a progressive neurological disorder affecting movement and speech,
necessitating early detection for better treatment outcomes. This research proposes a machine learningbased approach for PD detection using both image and speech data, including MRI scans, spiral
drawings, and speech recordings from diagnosed individuals and healthy controls. Feature extraction
techniques such as Mel-Frequency Cepstral Coefficients (MFCC) for speech and texture-based analysis
for images are applied to enhance model performance. The dataset undergoes preprocessing to remove
noise and standardize features before classification using Support Vector Machine (SVM), Random
Forest (RF), and Decision Tree (DT). Model evaluation based on accuracy, precision, recall, and F1-
score reveals that SVM achieves the highest accuracy, followed by RF and DT. Results indicate that
integrating multimodal data improves PD detection accuracy, offering a non-invasive and cost-effective
diagnostic solution. This study contributes to AI-driven medical diagnostics and paves the way for future
research incorporating deep learning for enhanced detection.
