# A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes
## 4 Discussion  
In the present study, we developed various screening models for undiagnosed diabetes and compared our models with each other as well as with models from other studies. The DLM had a higher AUC than any other model. Previous studies have predicted undiagnosed diabetes patients using various screening models, resulting in adequate goodness of fit and AUC [3; 8; 9; 10; 11]. [PERSON] et al. established a simple and effective risk score for type 2 diabetes mellitus in middle-aged rural Chinese [8]. [PERSON] et al. developed a risk score model for predicting diabetes in the Thai population that does not require laboratory tests [9]. [PERSON] et al. generated a simple risk model based on a non-invasive and an invasive model for type 2 diabetes [10]. [PERSON] et al. constructed a diabetes risk score for screening undiagnosed diabetes in Chinese adults and compared fasting capillary blood glucose (FCG) and glycated hemoglobinA1c (HbA1c) [11]. [PERSON] et al. developed a predictive model to identify individuals with an increased risk of undiagnosed based on the Rotterdam Study [12]. [PERSON] et al. proposed a self-assessment score for diabetes risk based on the Korea National Health and Nutrition Examination Survey (KNHANES) 2001-2005 that showed good discrimination in comparison with non-Asian models [13]. Our data included fewer subjects than the data used in previous studies, and were collected only from Korean citizens. Nevertheless, our study focused on developing a deep neural network model to improve screening performance. Several previous studies have developed predictive models using machine learning algorithms. [PERSON] et al. analyzed the performance of machine learning algorithms that can classify diabetes patients using Pima Indians diabetes data from a UCI (University of California Irvine) machine repository standard dataset [22]. The architecture of a deep artificial neural network was proposed that would use 19 clinical features to automatically determine the health statuses of patients [23]. [PERSON] et al. developed a diagnostic model for type 2 diabetes based on an artificial neural network using the Pima Indians dataset [24]. The primary goal of such studies was to predict diabetes status of subjects that are not identified. Furthermore, invasive variables are unsuitable for universal application, and can cause a financial burden to some people or countries. Thus, the development of disease prediction or screening models should be user-centric. [PERSON] et al. developed a diabetes prediction model using non-invasive variables based on machine learning algorithms (decision tree, AdaBoost, support vector machine, Bayesian network, naive Bayesian) that showed an appropriate performance [38]. The DLM included seven NIVs (age, male gender, hypertension, family history of diabetes, smoking status, BMI, and waist circumference) that would be convenient for a layperson to use as a self-assessment of diabetes risk in the real world. These variables are highly correlated with undiagnosed diabetes in other studies [39; 40; 41; 42; 43; 44; 45]. Although blood analysis (including FPG and oral glucose tolerance test) are required to diagnose at-risk individuals based on the guidelines, our model can provide users with an estimation of their diabetes status without a medical diagnosis. In addition, our model allows people to carry out self-screening in terms of diabetes. It can provide services for recommending schedule arrangements with health care practitioners to people at high risk. In the DLM building step we used variables such as age, BMI, and waist circumference with continuous measurements. However, we did not convert them into categorical values. Previous studies have applied a discretization approach that shows easily understandable information for screening diabetes risk. However, distinguished data by intervals may have biased information in the learning step. Our model insists on optimization based on hyper-parameters to prevent overfitting problems and improve model performance.