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The study demonstrated an application of machine learning techniques in building a depression prediction model. We used the NSHAP II data (3,377 subjects and 261 variables) and built the models using a logistic regression with and without L1 regularization. Depression prediction rates ranged 58.33% to 90.48% and 83.33% to 90.44% in the model with and without L1 regularization, respectively. The moderate to high prediction rates imply that the machine learning algorithms built the prediction models successfully.

Predicting Depression Among Community Residing Older Adults: A Use of Machine Learning Approch