Machine learning vs. conventional methods for prediction of 30-day readmission following percutaneous mitral edge-to-edge repair.

ABSTRACT:

Background

Identifying predictors of readmissions after mitral valve transcatheter edge-to-edge repair (MV-TEER) is essential for risk stratification and optimization of clinical outcomes.

Aims

We investigated the performance of machine learning [ML] algorithms vs. logistic regression in predicting readmissions after MV-TEER.

Methods

We utilized the National-Readmission-Database to identify patients who underwent MV-TEER between 2015 and 2018. The database was randomly split into training (70 %) and testing (30 %) sets. Lasso regression was used to remove non-informative variables and rank informative ones. The top 50 informative predictors were tested using 4 ML models: ML-logistic regression [LR], Naive Bayes [NB], random forest [RF], and artificial neural network [

SUBMITTER: Sulaiman S 

PROVIDER: S-EPMC10762683 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature