Comparative Study of Regression Techniques in the Estimation of UPDRS Score for Parkinsonâ€™s disease
Keywords:UPDRS, Parkinsonâ€™s disease, Robust Regression, Multilinear Regression, LASSO Regression, Ridge Regression, Shimmer, Jitter, Voice measures, motor UPDRS
Studies have shown that instances of Parkinsonâ€™s disease have been on the rise over the past 30 years. A metric that measures the extremity of Parkinsonâ€™s disease in a person is their Unified Parkinsonâ€™s Disease Rating Scale (UPDRS) score. Thus, an algorithm that can predict the UPDRS score of a Parkinsonâ€™s patient will be effective in determining the severity of the patientâ€™s condition. This paper aims to forecast a patientâ€™s UPDRS score by inferring patterns from historical figures and other independent parameter values that affect the patientsâ€™ UPDRS score. Four regression techniques namely multilinear, ridge, robust and LASSO regression are being used to predict the UPDRS scores. This will be done using the R language and through the use of the MASS, glmnet packages.
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