## 5 Experimental Results
Based on the finally selected 29 variables included in the KNHANES VI (2013, 2014, 2015) dataset, we performed a classification of the five chronic diseases (hypertension, stroke, myocardial infarction, angina pectoris, and diabetes mellitus) that affect many individuals but do not yet have clear predictive criteria. Figure 3 depicts a graph of the optimal learning frequency of the analytical model. The number of iterations during the learning phase was set at 50,000, because data loss increased when the number of iterations exceeded 50,000. Char-RNN was compared against multilayer perceptron (MLP), an extensively employed deep neural network model that was specifically developed for data classification. For MLP, three hidden layers were formed with 256, 128, and 64 nodes. The prediction accuracy was higher for Char-RNN than for MLP. Table 4 shows the accuracies of chronic disease predictions in 100, 200, and 300 test datasets with missing values based on the outcomes of learning via DNN and Char-RNN. In testing the model using test data, data imputation was performed on the models other than Char-RNN. KNN-imputation uses k-nearest neighbor and multiple imputation. KNN-imputation finds k-nearest neighbors with missing data, and then finds k-missing neighbors. This was used to find the class of data. There are several ways to measure the distance of neighbor algorithms. In this paper, we used Euclidean distance to find the closest neighbor and then used that value as a replacement for missing data. Alternatively, KNN with k neighbors can be used to take the weighted average of the distance from neighbors as a weight. The closer you are to neighbors, the more weight you have when you average. Weighted averages seem to be the most commonly used method [37]. Multiple imputation consists of  
Figure 3: Data loss at (**a**) 50,000 and (**b**) 55,000 epochs. three steps: imputation, analysis, and pooling. Multiple imputation can be used to account for the uncertainty of results in all environments. It can be interpreted as multiple substitution using chain equations. Therefore, we simulated multiple imputation using existing data, created several missing value substitution sets (m), performed specific statistical modeling with functions in the analysis step, and averaged m sets of substitutions generated in the pooling step to derive the results. This found the most optimal missing data replacement value [38]. The variables (HE_HPdg, HE_DMdg, HE_HLdg, and HE_fh) were physician diagnoses; hence, they directly affected the prediction outcome. The brightness contrast in the confusion matrices indicated that Char-RNN performed better than other models in predicting chronic diseases. Overall, the accuracy and precision were higher for Char-RNN, and the recall level was similar between the two models. The predictive power of Char-RNN was particularly high for hypertension and stroke. The accuracy was higher for Char-RNN compared to DNN, Bayesian, SVM, and long short-term memory (LSTM) models (Tables 3 and 4), most likely because other models classify new data based on learning the training data, whereas Char-RNN learns training data by treating words as a data pattern and attributes meaning to a word when encountering a similar word in each label. Therefore, Char-RNN is far more effective than other models in handling missing values. In addition, it can learn long sequences exceptionally well even when there is missing information, because it learns the training data by dividing sequences into n-grams. The missing values of test datasets of other models were solved through data imputation. For the data imputation method, we processed the missing values using KNNimpute, mode impute, and multiple impute methods.