Analysis of Vertical Ground Reaction Force Data in Predicting Parkinson’s Disease

Introduction: Parkinson’s disease is a complex, progressive neurodegenerative disorder known to negatively impair patient gait. Therefore, with gait and vertical ground reaction force data, an association can be made between the data and Parkinson’s disease. Methods: Data from 146 participants; 93 with Parkinson’s disease and 73 without Parkinson’s disease was obtained from a PhysioNet database for use in this article. A Fourier Analysis and several support vector machine learning models were computed in MATLAB to classify whether an individual had Parkinson’s disease. Results: From the Fourier analysis, it was determined that a statistically significant difference was present between the vertical ground reaction force data of individuals with and without Parkinson’s disease. Additionally, it was found that a Minimum Classification Error Optimized SVM machine learning model using Bayesian statistics was able to classify individuals with Parkinson’s disease using vertical ground reaction force data at an accuracy of 67.1%, and sensitivity of 80.43%. Conclusion: Therefore, it can be determined that vertical ground reaction force can predict Parkinson’s Disease with considerable accuracy which could be improved with an increased number of participants.