Development and external validation of machine learning approaches for risk prediction of cardiovascular disease in individuals with schizophrenia: a nationwide Swedish and Danish study.

ABSTRACT:

Background

Currently available cardiovascular disease (CVD) risk prediction tools may underestimate the risk in individuals with schizophrenia.

Objective

To develop and externally validate 5-year CVD risk prediction models for people with schizophrenia using large-scale register data in Sweden and Denmark with a machine learning (ML) approach.

Methods

Individuals with a diagnosis of schizophrenia, aged 30 and older and without prior CVD, were followed for up to 5 years. We investigated whether adding additional health-related and socio-demographic predictors to the established CVD risk factors improved predictions and compared ML models with logistic regression. External validation was performed across countries.

Findings

A lasso penalised logistic regression i

SUBMITTER: Nielsen SD 

PROVIDER: S-EPMC12815116 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature