Identifying risk factors of developing type 2 diabetes from an adult population with initial prediabetes using a Bayesian network

Abstract

BackgroundIt is known that people with prediabetes increase their risk of developing type 2 diabetes (T2D), which constitutes a global public health concern, and it is associated with other diseases such as cardiovascular disease.MethodsThis study aimed to determine those factors with high influence in the development of T2D once prediabetes has been diagnosed, through a Bayesian network (BN), which can help to prevent T2D. Furthermore, the set of features with the strongest influences on T2D can be determined through the Markov blanket. A BN model for T2D was built from a dataset composed of 12 relevant features of the T2D domain, determining the dependencies and conditional independencies from empirical data in a multivariate context. The structure and parameters were learned with the bnlearn package in R language introducing prior knowledge. The Markov blanket was considered to find those features (variables) which increase the risk of T2D.ResultsThe BN model established the different relationships among features (variables). Through inference, a high estimated probability value of T2D was obtained when the body mass index (BMI) was instantiated to obesity value, the glycosylated hemoglobin (HbA1c) to more than 6 value, the fatty liver index (FLI) to more than 60 value, physical activity (PA) to no state, and age to 48–62 state. The features increasing T2D in specific states (warning factors) were ranked.ConclusionThe feasibility of BNs in epidemiological studies is shown, in particular, when data from T2D risk factors are considered. BNs allow us to order the features which influence the most the development of T2D. The proposed BN model might be used as a general tool for prevention, that is, to improve the prognosis.

A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus

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Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network

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Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network

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