###### Abstract  
The aim of this study was to predict chronic diseases in individual patients using a character-recurrent neural network (Char-RNN), which is a deep learning model that treats data in each class as a word when a large portion of its input values is missing. An advantage of Char-RNN is that it does not require any additional imputation method because it implicitly infers missing values considering the relationship with nearby data points. We applied Char-RNN to classify cases in the Korea National Health and Nutrition Examination Survey (KNHANES) VI as normal status and five chronic diseases: hypertension, stroke, angina pectoris, myocardial infarction, and diabetes mellitus. We also employed a multilayer perceptron network for the same task for comparison. The results show higher accuracy for Char-RNN than for the conventional multilayer perceptron model. Char-RNN showed remarkable performance in finding patients with hypertension and stroke. The present study utilized the KNHANES VI data to demonstrate a practical approach to predicting and managing chronic diseases with partially observed information. Keywords:Human factor; deep learning; character recurrent neural network; statistic learning; health care; chronic disease; data mining; analysis +
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chlorella, which is reportedly effective in facilitating growth and improving stress-related ulcers in individuals at high risk for chronic disease. A study evaluating the effects of chlorella use found that this supplement improved fat metabolism and lowered blood glucose levels, suggesting that it may have beneficial effects in preventing chronic disease [3]. Disease development may also be influenced by an individual's living environment. A recent study quantified many diseases and risk factors that correspond to environmental variables by conducting correlation analyses on stress-related variables and chronic diseases [4]. As more health information data become available, a number of machine learning approaches have been implemented [2; 3; 4] to predict the characteristics of chronic disease potential using data as input variables and to predict these as individual medical histories. However, studies of chronic disease are usually experimental; hence, the resulting datasets tend to contain many missing values. Consequently, researchers are unlikely to obtain complete medical records and relevant information when analyzing chronic diseases. However, to the best of our knowledge, only a few approaches have predicted chronic diseases when there are missing values, and most of them have focused on handling them by imputation instead of implicit treatment. This study aims to accurately predict chronic diseases when there are missing values by using the character-recurrent neural network (Char-RNN). Char-RNN is a deep learning analysis method that specializes in text learning. It learns the relationships among sentences and the words they contain. Char-RNN learns sequences more efficiently than traditional machine learning methods because it learns sequences by character basis. In addition, it has a high performance for missing data analysis because it learns the sequence of data in the data learning where the missing value exists and learns it according to the rule of the preceding and succeeding words. If an incorrect sentence is identified, it finds the meaning of the sentence by the learned relationships between words and sentences and generates the correct sentence with a similar meaning for the input. Char-RNN has not been employed to deal with missing data, although it can implicitly treat unknown values. Thus, we applied Char-RNN to the Korea National Health and Nutrition Examination Survey (KNHANES) VI dataset (Supplementary Materials), which contains a large amount of missing information, to predict five types of chronic disease.