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

ABSTRACT:

Introduction

Depression 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.

Methods

This study aimed to determine the ability of machine learning-based decision s

SUBMITTER: Hosseinzadeh Kasani P 

PROVIDER: S-EPMC10203418 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature