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. The first-order component and second-order cross-feature component are then consolidated and applied to the chosen feature matrix to learn the associations between the physical examination data, respectively. The prediction is lastly produced by aggregating the representations. Extensive experimental results reveal that the proposed method's diagnostic prediction performance is superior to other state-of-the-art methods. Specifically, the Vitamin B12 indicator exhibits the strongest correlation with the early stage of atherosclerosis, while several known relevant biomarkers also demonstrate significant correlation in experimental data. The method proposed in this paper is a standalone tool, and its source code will be released in the future.

Keywords: atherosclerosis diagnosis; clinical data; early prediction; incomplete examination item; medical ambient intelligence.