Identifying major depressive disorder among US adults living alone using stacked ensemble machine learning algorithms.

ABSTRACT:

Background

It has been increasingly recognized that adults living alone have a higher likelihood of developing Major Depressive Disorder (MDD) than those living with others. However, there is still no prediction model for MDD specifically designed for adults who live alone.

Objective

This study aims to investigate the effectiveness of utilizing personal health data in combination with a stacked ensemble machine learning (SEML) technique to detect MDD among adults living alone, seeking to gain insights into the interaction between personal health data and MDD.

Methods

Our data originated from the US National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2018. We finally selected a set of 30 easily accessible variables encompassing demographic profil

SUBMITTER: Chen Z 

PROVIDER: S-EPMC11892380 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature