Scholarlink

<p>Parkinson’s disease (PD) causes motor, non-motor, and mental health challenges that significantly impact health-related quality of life (HRQoL); however, previous studies relied on subjective assessments. Several factors are associated with poor HRQoL, but the causal relationships remain unclear. Therefore, we aimed to identify key symptom factors 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. Key HRQoL factors were identified using a weighted ensemble of 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, fall efficacy scale-Korean, 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 non-motor factors 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multidimensional factors of health-related quality of life in parkinson’s disease using ensemble learning and network analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月21日

查看全文
  • Juseon Hwang,
  • Changhong Youm,
  • Hwayoung Park,
  • Bohyun Kim,
  • Hyejin Choi,
  • Minsoo Kim,
  • Sang-Myung Cheon

摘要

Parkinson’s disease (PD) causes motor, non-motor, and mental health challenges that significantly impact health-related quality of life (HRQoL); however, previous studies relied on subjective assessments. Several factors are associated with poor HRQoL, but the causal relationships remain unclear. Therefore, we aimed to identify key symptom factors 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. Key HRQoL factors were identified using a weighted ensemble of 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, fall efficacy scale-Korean, 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 non-motor factors 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.