WITHDRAWN Generalized Inference of Youden Index for Multi-Class Classification Applied to Parkinson's Disease

Music City Center 

Parkinson's Disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide. Accurate classification of PD severity using biomarker data is crucial for early diagnosis and disease monitoring. This study introduces a Generalized Variable Method (GVM)-based approach to improve statistical inference in multi-class classification problems, particularly in Parkinson's Disease classification using normally distributed biomarker data, in which patients are categorized into three or more stages of Parkinson's Disease (e.g., Mild = PD-N = Parkinson's Disease-Normal, Moderate = PD-MCI = Parkinson's Disease-Mild Cognitive Impairment, Severe = PD-D = Parkinson's Disease-Dementia) based on biomarker values. The proposed method ensures robust estimation of classification metrics, offering improved confidence interval estimation and decision-making strategies. We validate our approach through real-world biomarker datasets and Monte Carlo simulations, comparing its performance with traditional methods.

Multi-class Classification

Youden Index

Generalized Variable Method

Parkinson's Disease

Classical method

Machine Learning Approach 

Section on Statistical Computing