Multidimensional Predictors of Health-Related Quality of Life in Parkinson’s Disease Using Ensemble Learning and Network Analysis

Parkinson’s disease (PD) causes motor, nonmotor, and mental health challenges that significantly impact health-related quality of life (HRQoL), and most previous studies relied on subjective assessments. Several factors are associated with poor HRQoL; however, the causal relationships remain unclear. Therefore, we aimed to identify key symptom predictors of HRQoL and its subdomains in PD and examine the structure of their interaction networks. We assessed 101 individuals with PD in the ON medication state. HRQoL was measured using a weighted ensemble of the Least Absolute Shrinkage and Selection Operator and Extreme Gradient Boosting for feature selection, followed by stepwise multivariate linear regression. Network analysis was conducted to explore variable interrelations. The HRQoL total score was predicted by the Beck Anxiety Inventory (BAI), Fall Efficacy Scale-Korean (FES-K), Hoehn and Yahr scale, treatment duration, maximum jerk, and sample entropy, with an explanatory power of 65.7%. Regarding subdimensions, anxiety, fear of falling, and sample entropy of acceleration were key determinants. This study identifies key motor and nonmotor predictors of HRQoL in PD and reveals domain-specific networks. These findings may inform targeted interventions and clinical decision-making to improve HRQoL outcomes in people with PD.