Exploring Depression and Nutritional Covariates Amongst US Adults using Shapely Additive Explanations.

Background: Depression affects personal and public well-being and identification of natural therapeutics such as nutrition is necessary to help alleviate this public health concern.

Objective: The study aimed to identify feature importance in a machine learning model using solely nutrition covariates.

Methods: A retrospective analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020). Depressive symptoms were evaluated using the validated 9-item Patient Health Questionnaire (PHQ-9), and all adult patients (total of 7929 individuals) who completed the PHQ-9 and total nutritional intake questionnaire were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board.

Results: 7929 patients met the inclusion criteria in this study. The machine learning model had 24 out of a total of 60 features that were found to be significant on univariate analysis ( < 0.01 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.603, Sensitivity = 0.943, Specificity = 0.163. The top four highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Potassium Intake (Gain = 6.8%), Vitamin E Intake (Gain = 5.7%), Number of Foods and Beverages Reported (Gain = 5.7%), and Vitamin K Intake (Gain 5.6%).

Conclusion: Machine learning models with feature importance can be utilized to identify nutritional covariates for further study in patients with clinical symptoms of depression.