## 1 Introduction  
Gastric cancer is the fifth most common cancer in the world and one of the most frequently diagnosed cancers in South Korea [1, 2]. The national cancer screening program in Korea provides adults over the age of 40 with endoscopic screening once every two years, and the mortality rate is decreasing due to early gastric cancer detection and prompt treatment. However, endoscopic screening has adverse effects and can generate false-positive results that lead to overdiagnosis [3, 4]. Therefore, it is important to identify the groups that are at high risk of gastric cancer and to recommend regular endoscopic screening for these patients [5, 6]. Known risk factors for gastric cancer include infection with _Helicobacter pylori_ (_H. pylori_), salty and smoked food intake, smoking, alcohol, family history of gastric cancer, low socioeconomic status, and obesity [2, 7, 8]. Notably, _H. pylori_ infection was identified as a major cause of gastric cancer [9], and atrophic gastritis is a precursor that progresses to gastric cancer, although in less than 10% of cases [10]. Therefore, it is also necessary to identify the risk factors and predict the cases of atrophic gastritis that are at high risk of developing into gastric cancer. In this study, we proposed a deep recurrent neural network-based prediction model to identify risk factors for progression from atrophic gastritis to gastric cancer and to elucidate patients with atrophic gastritis who are at high risk of gastric cancer. Deep learning has wide utilization in health care applications such as medical imaging, screening, biomarker selection, and electronic health record (EHR) analysis [11; 12; 13]. In particular, predicting patient status using EHRs with various machine learning techniques such as support vector machine (SVM), random forests, and deep learning has attracted much attention [14; 15; 16]. Researchers have used EHRs and deep learning to predict pneumonia risk, hospital readmission, clinical events, and patients' future [17; 18; 19]. The proposed predictive model has explainability, which is the ability to explain how a prediction has been obtained using important features for each patient. Typically, deep learning-based predictive models show high accuracy, but they have disadvantages in their explainability [20; 21]. However, for medical applications, explanations are essential for both doctors and patients to understand and trust the predictive model's prediction results. Thus, explainability is essential for predictive models in medical applications [22]. In this paper, we present DeepPrevention, which consists of a prediction module and an explanation module. The prediction module performs a deep recurrent neural network (RNN)-based prediction, which predicts the probability that atrophic gastritis will develop into gastric cancer. The explanation module explains the prediction results, which identify risk factors for progression to gastric cancer from atrophic gastritis using the Chi-squared test. Furthermore, it detects high-risk patients who have a high probability of progressing to gastric cancer using K-means clustering [23]. Finally, the explanation module provides a personal explanation of the prediction using a local surrogate called LIME [24]. The data set used in this study consisted of South Korea National Health Insurance Service (NHIS) medical checkup data for patients with atrophic gastritis from 2002 to 2013. The NHIS data were converted to the Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) by Observational Health Data Sciences and Informatics [25]. A total of 29,557 patients with atrophic gastritis were identified, and among them, 771 had progressed to gastric cancer. As gastroscopy is recommended for adults aged 40 and older, we restricted the patient age range to 40-75 years; this resulted in 18,846 atrophic gastritis patients and 610 gastric cancer patients undergoing our analyses.