LAUSR

Abstract Parkinson’s disease (PD) causes gait impairments resulting in tremor, balance instabilities, increased fall risk, and disability. The current clinical diagnosis success rate is around 80%. Thus, automated classification of these impairments in gait with machine learning techniques can serve as an assessment tool for identification of PD. The primary focus of the study is to investigate anthropometric parameter-based models for classification of non-PD and PD subjects together with their severity. The proposed work performs the computation of clinically relevant features using Vertical Ground Reaction Force (VGRF) data from a total of 165 individuals’ database consisting 93 Parkinson’s and 72 healthy controls. All extracted features are tested for significance and redundancy among other gait characteristics. The optimal combination of features is selected using Recursive Feature Elimination technique with 10-fold cross validation for classification. In this study, wide range of machine learning techniques from different domains are used and their performance is evaluated based on accuracy, specificity, recall, precision and F1-score. Both gender and age-gender specific models outperformed the generalized model for PD as well as PD severity assessment. The highest prediction accuracy reported for age-gender specific models is 98.50% (non-PD and PD) using Support Vector Machine (SVM) classifier and 97.76% (non-PD and PD with severity scales) using k-Nearest Neighbor (kNN) and SVM. This study demonstrates integration of gait data with machine learning techniques as a potential biomarker for assessment of PD severity.