# A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes
## 1 Introduction  
Globally, an estimated 422 million adults are suffering from diabetes mellitus (DM), according to the World Health Organization Global Report on Diabetes. This number is significantly higher than that of 1980 (108 million) [1]. However, an estimated 30-80 percent of diabetes cases are undiagnosed [2]. Diabetes without clinical care is significantly linked to serious complications, which can add a considerable burden to the public health system. The prevalence of diabetes is expected to increase rapidly in the future due to the prevalence of obesity, aging of the population, and other cardiovascular risk factors [3].  
Complications of diabetes mellitus such as cardiovascular disease, kidney damage, and so on should be prevented in early stage [4]. However, diabetes is usually asymptomatic [5, 6]. People with undiagnosed diabetes are more likely to be diagnosed with complications than those who are aware of their diabetes status. Although fasting plasma glucose (FPG), the oral glucose tolerance test (OGTT),and hemoglobin A1C (HBA1c) are well-established determinants in diabetes diagnosis [7], they are insufficient to provide invasive screen tests for a large population [8].  
Risk screening systems for patients with undiagnosed diabetes has been developed [3; 8; 9; 10; 11; 12]. [PERSON] et al. developed a self-assessment score for diabetes risk in Korean adults [3]. [PERSON] et al. proposed a diabetes screening model for middle-aged rural Chinese [8]. [PERSON] et al. developed a prediction risk score for people at high risk of diabetes in Thailand [9]. [PERSON] et al. developed the model to predict the three-year incidence of type 2 diabetes in a Japanese population [10]. [PERSON] et al. constructed a diabetes risk score for screening undiagnosed diabetes and validated it using Chinese adults [11]. [PERSON] et al. developed a predictive model in order to identify individuals who had an increased risk of undiagnosed diabetes [12]. Theses system are used to prevent diabetes through changes in lifestyle and intervention with pharmaceutical treatments [13]. However, research using machine learning technology to develop screening tools for undiagnosed diabetes has been insufficient.  
Previous studies have introduced predictive models for diseases such as diabetic retinopathy, skin cancer, lung disease, heart failure, chronic kidney disease, and so on using machine learning techniques [14; 15; 16; 17; 18; 19; 20]. These studies that use deep learning techniques to make major advances in solving problems have resisted the best attempts of the artificial intelligence community in many cases [21]. Although previous studies have developed predictive models based on machine learning algorithms, it is unclear whether these models can be properly used for estimating undiagnosed diabetes [22; 23; 24]. Consequently, the objective of the present study was to develop a deep learning model (DLM) for patients with undiagnosed diabetes. The remainder of this paper is organized as follows. Section 2 describes the proposed framework, study design, and methods. Section 3 shows the result of the experiment and Section 4 presents the conclusion and discussion.