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.