Binary Grey Wolf Optimizer Biased Feature Selection for Parkinson's Disease Detection

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder that leads to patients’ motor and non-motor disabilities, making early detection challenging. One emerging method for diagnosing PD involves analyzing hand-drawing patterns, such as drawing spirals, circles, or specific shapes. Recent advances in computational techniques, particularly machine learning and deep learning algorithms, have been applied to quantify and assess these drawings, allowing for the differentiation between healthy individuals and those with early-stage PD. In this paper, we propose a multi-modal deep learning model for Parkinson’s disease detection using circles, spirals, and meanders drawings from the NewHandPD database. The proposed approach involves three key stages. First, a dedicated deep learning model is assigned to the three modalities to extract features from the hand-drawn data. These extracted features are then concatenated and fed into a fully connected layer, followed by a softmax classifier for final prediction. To further enhance detection accuracy, the Binary Grey Wolf Optimizer is integrated into the training process to perform feature selection. This method evaluates different combinations of features during training and selects the best subset based on the accuracy maximization. Simulation results using the NewHandPD dataset demonstrated an improvement in Parkinson's disease detection through multimodal and multi-deep learning classification. Furthermore, the introduction of feature selection significantly improved accuracy and reduced the feature vector length.