Development and validation of machine learning models based on stacked generalization to predict psychosocial maladjustment in patients with acute myocardial infarction.

ABSTRACT:

Background

Psychosocial maladjustment threatens the recovery of patients with acute myocardial infarction (AMI), and early identification of patients with psychosocial maladjustment may facilitate provision of reference to targeted interventions. The aims of this study were to: (1) identify key factors influencing patient psychosocial maladjustment, and (2) develop a machine learning predictive model based on Stacked Generalization.

Methods

Young and middle-aged patients with AMI (n = 734) were recruited from two tertiary hospitals (Center I and Center II) in Guangdong Province. Sociodemographic Characteristics, Perceived Stress Scale, Fear of Progression Questionnaire-Short Form, and Social Support Rating Scale data were collected before discharge, and psychosocial adjustme

SUBMITTER: Wang YF 

PROVIDER: S-EPMC11841291 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature