Parkinson’s Disease Prediction Using Convolutional Neural Networks and Hand-Drawn Image Analysis

Abstract

Parkinson’s Disease (PD) is a serious neurodegenerative disorder, with over 10 million cases globally in 2020, significantly affecting patients’ quality of life. The progression of the disease has been statistically proven, underscoring the importance of early diagnosis. As many as 80% of those diagnosed with PD begin to experience spinal degeneration, leading to other impairments and disabilities within approximately 10 years. Moreover, up to 35% of patients require assistance to walk or perform daily living activities within 5 years of diagnosis. The proposed study employs a neural convolutional network (CNN) to predict PD using 64x64 pixel hand-drawn images from 244 PD patients and 228 healthy individuals. K-nearest neighbors (KNN)-based feature extraction was applied as a data pre-processing method before feeding the data into the CNN layers. Model training involved tuning hyper parameters and testing several learning rates, ranging from 0.1 to 0.00001. The highest learning rate of 0.001 yielded the best performance, achieving classification accuracies, precision, sensitivity, and F1 score of 97.93%, 92%, 80%, and 86%, respectively, with a 5% increase in performance accuracy. These results demonstrate the model’s effective ability to discriminate between healthy individuals and PD patients based on hand-drawn samples.