Predicting the relationship between meal frequency and type 2 diabetes : empirical study using machine and deep learning

Type 2 diabetes (T2D) is a persisting issue affecting millions worldwide. Therefore, developing effective prevention and management strategies for T2D is crucial to improve the health and quality of life of individuals affected by this condition. Due to its significant impact on the public health sector, the researchers focused on developing predictive models to predict T2D and identify potential risk factors associated with the development of the condition. Predictive models for T2D have been proposed using various approaches, including machine learning and deep learning algorithms. Using traditional statistical methods, these models can analyze large datasets and identify patterns that may not be apparent. Some models consider factors such as age, sex, family history, lifestyle habits, and medical history to predict the risk of developing T2D. However, the relationship between meal frequency and blood sugar level remains a controversial research topic. The appropriate meal frequency depends on an individual's health information and diet habits. Therefore, investigating the hypothesis that meal frequency significantly impacts diabetes prediction, but the relationship is not simply linear, could be helpful. In this study, we aim to build a diabetes predictive model using machine learning methods. We investigate the relationship between meal frequency and the incidence rate of type 2 diabetes using data from the Korean National Health and Nutrition Examination Survey (KNHANES) 2013-2014. Furthermore, we employ seven machine learning and deep learning algorithms to verify the hypothesis. Our experiments reveal the best-performing model, demonstrating a prediction accuracy of approximately 0.818. Interestingly, our results show that meal frequency has low importance. We compare our experimental results with state-of-the-art models and discuss the different conclusions these studies report.