Does an explainable machine learning method accurately classify patients with depressive disorder based on nutritional status?
An explainable machine learning model using nutritional status can accurately classify patients with clinical depression.
The 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.
Kasani et al. (Tue,) studied this question.