Open AccessPrediction of atherosclerosis using machine learning based on operations research
Author(s) -
Zihan Chen,
Mei Yang,
Yuhang Wen,
Songyan Jiang,
Wenjun Liu,
Hui Huang
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022229
Subject(s) - redundancy (engineering) , machine learning , artificial intelligence , disease , myocardial infarction , computer science , graph , correlation , medicine , mathematics , geometry , theoretical computer science , operating system
Atherosclerosis is one of the major reasons for cardiovascular disease including coronary heart disease, cerebral infarction and peripheral vascular disease. Atherosclerosis has no obvious symptoms in its early stages, so the key to the treatment of atherosclerosis is early intervention of risk factors. Machine learning methods have been used to predict atherosclerosis, but the presence of strong causal relationships between features can lead to extremely high levels of information redundancy, which can affect the effectiveness of prediction systems.