Title: A causal model for type 2 diabetes and its comparison with other modelling methods

Authors: Sheng Zhang; Xiangdong An; Hai Wang

Addresses: School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, Shandong, 250357, China ' Department of Computer Science, University of Tennessee at Martin, Martin, TN 38237, USA ' Department of Finance, Information Systems, and Management Science, Saint Mary's University, Halifax, Nova Scotia, B3H 3C3, Canada

Abstract: In this paper, we investigate probabilistic graphical models for risk modelling and assessment of type 2 diabetes. In particular, we study a new cause-effect model and focus on the impacts of life styles and socioeconomics to type 2 diabetes. The proposed model encodes cause-effect dependencies instead of correlations or conditional independencies among variables, which is different from previous work. Experiments on a large healthcare dataset show that the proposed causal modelling method significantly outperforms the baseline naive Bayesian network (BN) models and performs similarly to the conventional conditional independency modelling BNs and correlation modelling logistic regression models. The proposed model has the advantage of modelling cause-effect relationships over other models.

Keywords: risk assessment; Bayesian networks; diabetes.

DOI: 10.1504/IJEH.2020.113198

International Journal of Electronic Healthcare, 2020 Vol.11 No.2, pp.157 - 169

Accepted: 13 Nov 2020
Published online: 23 Feb 2021 *

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