{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:20:50Z","timestamp":1778692850739,"version":"3.51.4"},"reference-count":22,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This study introduces BagStacking, an innovative ensemble learning framework designed to enhance the detection of freezing of gait (FOG) in Parkinson\u2019s disease (PD) using accelerometer data. By synergistically combining bagging\u2019s variance reduction with stacking\u2019s 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\u2019s effectiveness in addressing the variability inherent in FOG detection, thereby contributing to improved patient care in PD.<\/jats:p>","DOI":"10.3390\/info15120822","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T10:58:55Z","timestamp":1734951535000},"page":"822","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["BagStacking: An Integrated Ensemble Learning Approach for Freezing of Gait Detection in Parkinson\u2019s Disease"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1135-0079","authenticated-orcid":false,"given":"Seffi","family":"Cohen","sequence":"first","affiliation":[{"name":"Software and Information Systems Engineering, Ben-Gurion University, David Ben-Gurion Blvd. 1, Beer Sheva 84105, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nurit","family":"Cohen-Inger","sequence":"additional","affiliation":[{"name":"Software and Information Systems Engineering, Ben-Gurion University, David Ben-Gurion Blvd. 1, Beer Sheva 84105, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6956-3341","authenticated-orcid":false,"given":"Lior","family":"Rokach","sequence":"additional","affiliation":[{"name":"Software and Information Systems Engineering, Ben-Gurion University, David Ben-Gurion Blvd. 1, Beer Sheva 84105, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Naghavi, N., Miller, A., and Wade, E. 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