Introduction: Depression is a global concern, with a significant number of people affected worldwide, particularly in low- and middle-income countries. The rising prevalence of depression emphasizes the importance of early detection and understanding the origins of such conditions.
Objective: This paper proposes a framework for detecting depression using a hybrid visualization approach that combines local and global interpretation. This approach aims to assist in model adaptation, provide insights into patient characteristics, and evaluate prediction model suitability in a different environment.
Methods: This study utilizes R programming language with the Caret, ggplot2, Plotly, and Dalex libraries for model training, visualization, and interpretation. Data from the NHANES repository was used for secondary data analysis. The NHANES repository is a comprehensive source for examining health and nutrition of individuals in the United States, and covers demographic, dietary, medication use, lifestyle choices, reproductive and mental health data. Penalized logistic regression models were built using NHANES 2015-2018 data, while NHANES 2019-March 2020 data was used for evaluation at the global-specific and local level interpretation.
Results: The prediction model that supports this framework achieved an average AUC score of 0.748 (95% CI: 0.743-0.752), with minimal variability in sensitivity and specificity.
Conclusion: The built-in prediction model highlights chest pain, the ratio of family income to poverty, and smoking status as crucial features for predicting depressive states in both the original and local environments.
Keywords: Depression; Explainable; Feature importance; Hybrid visualization; Interpretation; Shapley.
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