AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease.

ABSTRACT: Coronary heart disease (CHD) is one of the leading causes of mortality and morbidity in the United States. Accurate time-to-event CHD prediction models with high-dimensional DNA methylation and clinical features may assist with early prediction and intervention strategies. We developed a state-of-the-art deep learning autoencoder survival analysis model (AESurv) to effectively analyze high-dimensional blood DNA methylation features and traditional clinical risk factors by learning low-dimensional representation of participants for time-to-event CHD prediction. We demonstrated the utility of our model in two cohort studies: the Strong Heart Study cohort (SHS), a prospective cohort studying cardiovascular disease and its risk factors among American Indians adults; the Women's Health Initiati

SUBMITTER: Shen Y 

PROVIDER: S-EPMC11424508 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature