Automatic Diagnosis of Parkinson’s Disease using Handwriting Patterns

Abstract

Parkinson's Disease (PD) is a neuro-degenerative syndrome characterized by motor and non-motor signs, and early detection is crucial for effective intervention. This paper presents a novel approach for PD detection using computer vision and machine learning techniques applied to Spiral-Wave handwriting analysis. The dataset comprises frontal handwritten images obtained through the Spiral-Wave test, capturing subtle motor control differences. Our methodology involves resizing images to a standardized 200x200 pixels, converting them to grayscale, and applying thresholding for improved feature abstraction. Histogram of Oriented Gradients (HOG) is employed to capture shape and texture information. The development of a strong approach for deriving significant features from Spiral-Wave handwriting patterns and the usage of machine learning classifiers for precise PD analysis are the two main goals of this work. The emphasis is on using Random Forest and K-Nearest Neighbours (KNN) classifiers for Spiral and Wave pictures, respectively, in conjunction with the Histogram of Oriented Gradients (HOG) approach for feature extraction. For Spiral images, a Random Forest Classifier is utilized, achieving an accuracy of 86.67%. The classifier's interpretability is enhanced through an analysis of feature importance, revealing critical HOG features for distinguishing between healthy and PD-afflicted patterns. The Wave images are classified using a K-Nearest Neighbours (KNN) model, attaining an accuracy of 76.67%. Performance metrics, including precision, recall, and F1-score, offer a nuanced assessment of the KNN model's capabilities.