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.