Identifying risk factors of developing type 2 diabetes from an adult population with initial prediabetes using a Bayesian network - Fuster-Parra, Pilar; Yañez, Aina M.; López-González, Arturo; Aguiló, A.; Bennasar-Veny, Miquel (Frontiers in Public Health, 2023)
It is known that people with prediabetes increase their risk of developing type 2 diabetes (T2D), which constitutes a globlal public health concern, and it is associated to other diseases such as cardiovascular disease, cancer, etc. The aim of this study was 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 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. The 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 Gycosylated Haemoglobin (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. The proposed BN model might be used as a general tool for prevention, that is, to improve the prognosis.