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Advancing diabetes prediction with a progressive self-transfer learning framework for discrete time series data
- Title
- Advancing diabetes prediction with a progressive self-transfer learning framework for discrete time series data
- Authors
- Lim, Heeryung; Kim, Gihyeon; Choi, Jang-Hwan
- Ewha Authors
- 최장환
- SCOPUS Author ID
- 최장환
- Issue Date
- 2023
- Journal Title
- SCIENTIFIC REPORTS
- ISSN
- 2045-2322
- Citation
- SCIENTIFIC REPORTS vol. 13, no. 1
- Publisher
- NATURE PORTFOLIO
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- Although diabetes mellitus is a complex and pervasive disease, most studies to date have focused on individual features, rather than considering the complexities of multivariate, multi-instance, and time-series data. In this study, we developed a novel diabetes prediction model that incorporates these complex data types. We applied advanced techniques of data imputation (bidirectional recurrent imputation for time series; BRITS) and feature selection (the least absolute shrinkage and selection operator; LASSO). Additionally, we utilized self-supervised algorithms and transfer learning to address the common issues with medical datasets, such as irregular data collection and sparsity. We also proposed a novel approach for discrete time-series data preprocessing, utilizing both shifting and rolling time windows and modifying time resolution. Our study evaluated the performance of a progressive self-transfer network for predicting diabetes, which demonstrated a significant improvement in metrics compared to non-progressive and single self-transfer prediction tasks, particularly in AUC, recall, and F1 score. These findings suggest that the proposed approach can mitigate accumulated errors and reflect temporal information, making it an effective tool for accurate diagnosis and disease management. In summary, our study highlights the importance of considering the complexities of multivariate, multi-instance, and time-series data in diabetes prediction.
- DOI
- 10.1038/s41598-023-48463-0|http://dx.doi.org/10.1038/s41598-023-48463-0
- Appears in Collections:
- 인공지능대학 > 인공지능학과 > Journal papers
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