Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets.

ABSTRACT: Heart disease is one of the primary causes of morbidity and death worldwide. Millions of people have had heart attacks every year, and only early-stage predictions can help to reduce the number. Researchers are working on designing and developing early-stage prediction systems using different advanced technologies, and machine learning (ML) is one of them. Almost all existing ML-based works consider the same dataset (intra-dataset) for the training and validation of their method. In particular, they do not consider inter-dataset performance checks, where different datasets are used in the training and testing phases. In inter-dataset setup, existing ML models show a poor performance named the inter-dataset discrepancy problem. This work focuses on mitigating the inter-dataset discrepancy p

SUBMITTER: Hasan M 

PROVIDER: S-EPMC11041935 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature