Predicting Parkinson's Disease Severity using Telemonitoring Data and Machine Learning Models: A Principal Component Analysis-based Approach for Remote Healthcare Services during COVID-19 Pandemic

Abstract

Parkinson's disease (PD) is a progressive and chronic neurological condition that affects about 1% of the world's over-60 population. The COVID-19 pandemic has emphasized the significance of remote healthcare services, such as telemedicine, in managing chronic diseases such as PD. This research intends to construct machine learning (ML) models to predict PD severity utilizing vocal data derived from the UCI database for motor and total Unified Parkinson's disease rating scale (UPDRS) ratings. ML was used to study the association between voice vibration and PD, and PCA and ML models were utilized to minimize model complexity and compare the predictive performance of various statistical models for PD regression. The dataset included 5,875 medical voice records from 42 patients with early-stage PD who participated in a six-month clinical trial. The proposed PCA model simplified the model and achieved a root-mean-square error of 1.78 with an R-squared value of 0.95 for predicting the motor UPDRS and 1.78 with an R-squared value of 0.97 for predicting the total UPDRS. This work can give a framework for developing remote healthcare services for Parkinson's disease and other chronic conditions, which can be helpful during pandemics and other situations where access to in-person care is limited.