Identifying latent variables in Dynamic Bayesian Networks with bootstrapping applied to Type 2 Diabetes complication prediction

Article type: Research Article

Authors: Yousefi, Leila^{a; *} | Tucker, Allan^b

Affiliations: [a] Department of Life Science, Brunel University London, UK | [b] Department of Computer Science, Brunel University London, UK

Correspondence: [*] Corresponding author: Leila Yousefi, Department of Life Science, Brunel University London, UK. E-mail: Lilyyousefi84@gmail.com.

Abstract: 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.

Keywords: Latent variable, diabetes, Dynamic Bayesian Networks, time series bootstrapping, disease prediction

DOI: 10.3233/IDA-205570

Journal: Intelligent Data Analysis, vol. 26, no. 2, pp. 501-524, 2022