Advancing diabetes prediction with a progressive self-transfer learning framework for discrete time series data.

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 r

SUBMITTER: Lim H 

PROVIDER: S-EPMC10687240 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature