Evaluation of nutritional status and clinical depression classification using an explainable machine learning method

Abstract

IntroductionDepression is a prevalent disorder worldwide, with potentially severe implications. It contributes significantly to an increased risk of diseases associated with multiple risk factors. Early accurate diagnosis of depressive symptoms is a critical first step toward management, intervention, and prevention. Various nutritional and dietary compounds have been suggested to be involved in the onset, maintenance, and severity of depressive disorders. Despite the challenges to better understanding the association between nutritional risk factors and the occurrence of depression, assessing the interplay of these markers through supervised machine learning remains to be fully explored.MethodsThis study aimed to determine the ability of machine learning-based decision support methods to identify the presence of depression using publicly available health data from the Korean National Health and Nutrition Examination Survey. Two exploration techniques, namely, uniform manifold approximation and projection and Pearson correlation, were performed for explanatory analysis among datasets. A grid search optimization with cross-validation was performed to fine-tune the models for classifying depression with the highest accuracy. Several performance measures, including accuracy, precision, recall, F1 score, confusion matrix, areas under the precision-recall and receiver operating characteristic curves, and calibration plot, were used to compare classifier performances. We further investigated the importance of the features provided: visualized interpretation using ELI5, partial dependence plots, and local interpretable using model-agnostic explanations and Shapley additive explanation for the prediction at both the population and individual levels.ResultsThe best model achieved an accuracy of 86.18% for XGBoost and an area under the curve of 84.96% for the random forest model in original dataset and the XGBoost algorithm with an accuracy of 86.02% and an area under the curve of 85.34% in the quantile-based dataset. The explainable results revealed a complementary observation of the relative changes in feature values, and, thus, the importance of emergent depression risks could be identified.DiscussionThe strength of our approach is the large sample size used for training with a fine-tuned model. The machine learning-based analysis showed that the hyper-tuned model has empirically higher accuracy in classifying patients with depressive disorder, as evidenced by the set of interpretable experiments, and can be an effective solution for disease control.

Predicting Depression during the COVID-19 Pandemic Using Interpretable TabNet: A Case Study in South Korea

“…The XGBoost model achieved high performance, with accuracy and AUC values of 0.8602 and 0.8534, respectively. However, compared to the TabNet model proposed in our study, our model outperformed the XGBoost model proposed by Hosseinzadeh Kasani et al [45] in terms of accuracy and AUC. In another study, by Zulfiker et al [47], six different machine learning classifiers were investigated using socio-demographic and psychosocial information from a revised version of the Burns Depression Checklist In a recent study by Hosseinzadeh Kasani et al [45], an XGBoost model was c structed to identify depression using a dataset from the Korean National Health and trition Examination Survey (K-NHANES) [46] The hyperparameter optimization carried out in this study also produced remarkable outcomes.…”

“…In a recent study by Hosseinzadeh Kasani et al [45], an XGBoost model was constructed to identify depression using a dataset from the Korean National Health and Nutrition Examination Survey (K-NHANES) [46] with 4,804 samples. The XGBoost model achieved high performance, with accuracy and AUC values of 0.8602 and 0.8534, respectively.…”

Comparative effectiveness of explainable machine learning approaches for extrauterine growth restriction classification in preterm infants using longitudinal data

“…The Pearson coefficient is an indicator used to measure the strength and direction of a linear relationship between given variables and responses (46). The heatmap generated by Pearson correlation has been commonly used in numerous research fields (47)(48)(49). The study conducted a correlation analysis to gain an initial understanding of the relationships between the predictor variables and the outcome variables in all datasets.…”