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Existing research has predominantly focused on predicting individual frailty-related outcomes. However, this paper takes a novel approach by framing frailty as a multi-label learning problem, aiming to predict multiple adverse outcomes simultaneously. In the context of multi-label classification, dealing with imbalanced label distribution poses inherent challenges to multi-label prediction. To address this issue, our study proposes a hybrid resampling approach tailored for handling imbalance problems in the multi-label scenario. The proposed resampling technique and prediction tasks were applied to a high-dimensional real-life medical dataset comprising individuals aged 65\u00a0years and above. Several multi-label algorithms were employed in the experiment, and their performance was evaluated using multi-label metrics. The results obtained through our proposed approach revealed that the best-performing prediction model achieved an average precision score of 83%. These findings underscore the effectiveness of our method in predicting multiple frailty outcomes from a complex and imbalanced multi-label dataset.<\/jats:p>","DOI":"10.1007\/s41666-024-00173-6","type":"journal-article","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T09:01:37Z","timestamp":1727859697000},"page":"594-618","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Multiple Outcomes Associated with Frailty based on Imbalanced Multi-label Classification"],"prefix":"10.1007","volume":"8","author":[{"given":"Adane Nega","family":"Tarekegn","sequence":"first","affiliation":[]},{"given":"Krzysztof","family":"Michalak","sequence":"additional","affiliation":[]},{"given":"Giuseppe","family":"Costa","sequence":"additional","affiliation":[]},{"given":"Fulvio","family":"Ricceri","sequence":"additional","affiliation":[]},{"given":"Mario","family":"Giacobini","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,2]]},"reference":[{"key":"173_CR1","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1186\/s12877-022-03194-1","volume":"22","author":"J Lv","year":"2022","unstructured":"Lv J, Li R, Yuan L, Yang X, Wang Y, Ye Z-W, Huang F-M (2022) Research on the frailty status and adverse outcomes of elderly patients with multimorbidity. 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