Machine learning and physical based modeling for cardiac hypertrophy.

ABSTRACT:

Background and objective

Predicting the long-term expansion and remodeling of the left ventricle in patients is challenging task but it has the potential to be clinically very useful.

Methods

In our study, we present machine learning models based on random forests, gradient boosting, and neural networks, used to track cardiac hypertrophy. We collected data from multiple patients, and then the model was trained using the patient's medical history and present level of cardiac health. We also demonstrate a physical-based model, using the finite element procedure to simulate the development of cardiac hypertrophy.

Results

Our models were used to forecast the evolution of hypertrophy over six years. The machine learning model and finite element model provided similar resul

SUBMITTER: Milicevic B 

PROVIDER: S-EPMC10258386 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature