Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study.

ABSTRACT:

Objective

Depression has emerged as a global public health concern with high incidence and disability rates, which are timely imperative to identify and intervene in clinical practice. The objective of this study was to explore the association between heart rate variability (HRV) and depression, with the aim of establishing and validating machine learning models for the auxiliary diagnosis of depression.

Methods

The data of 465 outpatients from the Affiliated Hospital of Southwest Medical University were selected for the study. The study population was then randomly divided into training and test sets in a 7:3 ratio. Logistic regression (LR), support vector machine (SVM), random forest (RF) and eXtreme gradient boosting (XGBoost) algorithm models were used to construct risk

SUBMITTER: Yang M 

PROVIDER: S-EPMC11654159 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature