Work

Predicting Multiple Outcomes Associated with Frailty based on Imbalanced Multi-label Classification

Year: 2024

Type: article

Abstract: Frailty syndrome is prevalent among the elderly, often linked to chronic diseases and resulting in various adverse health outcomes. 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 years 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.

Source: Journal of Healthcare Informatics Research

DOI: 10.1007/s41666-024-00173-6

Authors: Adane Nega Tarekegn, Krzysztof Michalak, Giuseppe Costa, Fulvio Ricceri, Mario Giacobini

Institutions: University of Bergen, Bahir Dar University, Wroclaw University of Economics and Business, University of Turin

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,390