BagStacking: An Integrated Ensemble Learning Approach for Freezing of Gait Detection in Parkinson’s Disease

Seffi Cohen, Nurit Cohen-Inger, Lior Rokach

Research output: Contribution to journalArticlepeer-review

Abstract

This study introduces BagStacking, an innovative ensemble learning framework designed to enhance the detection of freezing of gait (FOG) in Parkinson’s disease (PD) using accelerometer data. By synergistically combining bagging’s variance reduction with stacking’s sophisticated blending mechanisms, BagStacking achieves superior predictive performance. Evaluated on a comprehensive PD dataset provided by the Michael J. Fox Foundation, BagStacking attained a mean average precision (MAP) of 0.306, surpassing standalone LightGBM and traditional stacking methods. Furthermore, BagStacking demonstrated superior area under the curve (AUC) metrics across key FOG event classes. Specifically, it achieved AUCs of 0.88 for start hesitation, 0.90 for turning, and 0.84 for walking events, outperforming multistrategy ensemble, regular stacking, and LightGBM baselines. Additionally, BagStacking exhibited reduced runtime compared to other ensemble approaches, making it suitable for real-time clinical monitoring. These results underscore BagStacking’s effectiveness in addressing the variability inherent in FOG detection, thereby contributing to improved patient care in PD.

Original languageEnglish
Article number822
JournalInformation (Switzerland)
Volume15
Issue number12
DOIs
StatePublished - 1 Dec 2024

Keywords

  • bagging
  • ensemble
  • FOG
  • freezing of gait
  • IoT
  • Parkinson
  • PD
  • sensors
  • stacking

ASJC Scopus subject areas

  • Information Systems

Fingerprint

Dive into the research topics of 'BagStacking: An Integrated Ensemble Learning Approach for Freezing of Gait Detection in Parkinson’s Disease'. Together they form a unique fingerprint.

Cite this