Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/24382
Title: Identifying Latent Variables in Dynamic Bayesian Networks with Bootstrapping Applied to Type 2 Diabetes Complication Prediction
Authors: Leila, Y
Tucker, A
Keywords: latent variable;diabetes;dynamic Bayesian networks;time series bootstrapping;disease prediction
Issue Date: 14-Mar-2022
Publisher: IOS Press
Citation: Yousefi, L. and Tucker, A. (2022) ‘Identifying Latent Variables in Dynamic Bayesian Networks with Bootstrapping Applied to Type 2 Diabetes Complication Prediction’, Intelligent Data Analysis, 26 (2), pp. 501 - 524. doi: 10.3233/IDA-205570.
Abstract: Copyright © 2021 The Authors. Predicting complications associated with complex disease is a challenging task given imbalanced and highly correlated disease complications along with unmeasured or latent factors. To analyse the complications associated with complex disease, this article attempts to deal with complex imbalanced clinical data, whilst determining the influence of latent variables within causal networks generated from the observation. This work proposes appropriate Intelligent Data Analysis methods for building Dynamic Bayesian networks with latent variables, applied to small-sized clinical data (a case of Type 2 Diabetes complications). First, it adopts a Time Series Bootstrapping approach to re-sample the rare complication class with a replacement with respect to the dynamics of disease progression. Then, a combination of the Induction Causation algorithm and Link Strength metric (which is called IC*LS approach) is applied on the bootstrapped data for incrementally identifying latent variables. The most highlighted contribution of this paper gained insight into the disease progression by interpreting the latent states (with respect to the associated distributions of complications). An exploration of inference methods along with confidence interval assessed the influences of these latent variables. The obtained results demonstrated an improvement in the prediction performance.
URI: https://bura.brunel.ac.uk/handle/2438/24382
DOI: https://doi.org/10.3233/IDA-205570
ISSN: 1088-467X
Other Identifiers: ORCID iDs: Leila Yousefi https://orcid.org/0000-0003-1952-0674; Allan Tucker https://orcid.org/0000-0001-5105-3506.
Appears in Collections:Dept of Computer Science Research Papers

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