Applying Bayesian network for noncommunicable diseases risk analysis: Implementing national health examination survey in Thailand

Abstract

We propose using a Bayesian network to capture and understand the dependency risk factors affecting the prevalence of chronic diseases. By applying a Bayesian network model, we can visualize interdependencies between risks and their effects on the Noncommunicable disease (NCD) prevalence. By using a Bayesian network to model the prevalence of diabetes, we can define the top three risks as family history of diabetes, obesity, and age. Furthermore, the risk classification results can help to determine the managing strategy. For the Thai population, problems arising from family history of diabetes and obesity can be met by employing a transfer strategy. Age (especially ages of 35-59) and the risk incurred by low intake of fruits and vegetables should use a reduction or mitigation strategy. Finally, those at risk as a result of their area of residence (in urban areas) and socio-economic factors within the 4th quantile and low level of physical activity should apply a retain strategy.