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Abstract: Objectives: This study aims to suggest a depression prediction model using variables indirectly related to depression, which helps determine whether the target is depressed. Methods: This study using Korea Institute for Health and Social Affairs (KIHSA)’s 2011, 2014, 2017 National Survey of Older Koreans, and the total number of subjects of the study is 30,571 elderly people 65 years of age or older. Results: The analysis way is deep learning especially Wide & Deep Learning model that separately learns simple data and complex data. The dependent variable is depression, and the independent variable is a total of 30 variables related to personal attributes, economic status, human relationships, health status, health behavior, and life satisfaction. As a result of the main study, all the independent variables used were found to be correlated with depression, performance of Wide & Deep model is 77.9% accuracy, 65.7% recall, 73.8% precision, 85.5% specificity, 69.5% F1-score, 85.3% AUROC, 90.0% AUPRC. Conclusions: It is expected that it will be helpful for the welfare of the elderly by searching for depressed patients at a lower cost and searching for potential depressed patients who want to hide their depression.