Pooling random forest and functional data analysis for biomedical signals supervised classification: Theory and application to electrocardiogram data.

ABSTRACT: Scientific progress has contributed to creating many devices to gather vast amounts of biomedical data over time. The goal of these devices is generally to monitor people's health conditions, diagnose, and prevent patients' diseases, for example, to discover cardiovascular disorders or predict epileptic seizures. A common way of investigating these data is classification, but these instruments generate signals often characterized by high dimensionality. Learning from these data is definitely a challenging task due to many issues, for example, the trade-off between complexity and accuracy and the course of dimensionality. This study proposes a supervised classification method based on the joint use of functional data analysis, classification trees, and random forest to deal with massive bio

SUBMITTER: Maturo F 

PROVIDER: S-EPMC9303904 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature