Early prediction of atherosclerosis diagnosis with medical ambient intelligence.

ABSTRACT: Atherosclerosis is a chronic vascular disease that poses a significant threat to human health. Common diagnostic methods mainly rely on active screening, which often misses the opportunity for early detection. To overcome this problem, this paper presents a novel medical ambient intelligence system for the early detection of atherosclerosis by leveraging clinical data from medical records. The system architecture includes clinical data extraction, transformation, normalization, feature selection, medical ambient computation, and predictive generation. However, the heterogeneity of examination items from different patients can degrade prediction performance. To enhance prediction performance, the "SEcond-order Classifier (SEC)" is proposed to undertake the medical ambient computation task.

SUBMITTER: Yang W 

PROVIDER: S-EPMC10398961 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature