English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

The stage and severity of Parkinson's disease (PD) is an important factor toconsider for taking effective therapeutic decisions. Although the MovementDisorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) providesan effective instrument evaluating the most pertinent features of PD, it doesnot allow PD staging. On the other hand, the Hoehn and Yahr (HY) scale whichprovides staging, does not evaluate many relevant features of PD. In thispaper, we propose a novel and improved staging for PD using the MDS-UPDRSfeatures and the HY scale, and developing prediction models to estimate thestage (normal, early or moderate) and severity of PD using machine learningtechniques such as ordinal logistic regression (OLR), support vector machine(SVM), AdaBoost- and RUSBoost-based classifiers. Along with this, featureimportance in PD is also estimated using Random forests. We observe that thepredictive models of SVM, Adaboost-based ensemble, Random forests andprobabilistic generative model performed well with the AdaBoost-based ensemblegiving the highest accuracy of 97.46%. Body bradykinesia, tremor, facialexpression (hypomimia), constancy of rest tremor and handwriting (micrographia)were observed to be the most important features in PD. It is inferred thatMDS-UPDRS combined with classifiers can form effective tools to predict PDstaging which can aid clinicians in the diagnostic process.

Novel and Improved Stage Estimation in Parkinson's Disease using Clinical Scales and Machine Learning