|
|
Journal of Biostatistics and Epidemiology، جلد ۱۰، شماره ۳، صفحات ۳۲۷-۳۴۱
|
|
|
| عنوان فارسی |
|
|
| چکیده فارسی مقاله |
|
|
| کلیدواژه‌های فارسی مقاله |
|
|
| عنوان انگلیسی |
Analysis of Vertical Ground Reaction Force Data in Predicting Parkinson’s Disease |
|
| چکیده انگلیسی مقاله |
Background: Parkinson’s disease (PD) is a complex, progressive neurodegenerative disorder known to negatively impair patient gait. Therefore, with gait and vertical ground reaction force (VGRF) 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 VGRF 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 VGRF 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. |
|
| کلیدواژه‌های انگلیسی مقاله |
|
|
| نویسندگان مقاله |
| Varun Jain
McMaster University
|
|
| نشانی اینترنتی |
https://jbe.tums.ac.ir/index.php/jbe/article/view/1427 |
| فایل مقاله |
فایلی برای مقاله ذخیره نشده است |
| کد مقاله (doi) |
|
| زبان مقاله منتشر شده |
|
| موضوعات مقاله منتشر شده |
|
| نوع مقاله منتشر شده |
|
|
|
|
برگشت به:
صفحه اول پایگاه |
نسخه مرتبط |
نشریه مرتبط |
فهرست نشریات
|