Prediction of heart failure 1 year before diagnosis in general practitioner patients using machine learning algorithms: a retrospective case-control study.

ABSTRACT:

Objectives

Heart failure (HF) is a commonly occurring health problem with high mortality and morbidity. If potential cases could be detected earlier, it may be possible to intervene earlier, which may slow progression in some patients. Preferably, it is desired to reuse already measured data for screening of all persons in an age group, such as general practitioner (GP) data. Furthermore, it is essential to evaluate the number of people needed to screen to find one patient using true incidence rates, as this indicates the generalisability in the true population. Therefore, we aim to create a machine learning model for the prediction of HF using GP data and evaluate the number needed to screen with true incidence rates.

Design, settings and participants

GP data from 8543 pati

SUBMITTER: Bennis FC 

PROVIDER: S-EPMC9438066 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature