ARTIFICIAL INTELLIGENCE MODELS FOR PREDICTING CARDIOVASCULAR DISEASES IN PEOPLE WITH TYPE 2 DIABETES MELLITUS: ANALYTICAL REVIEW
Keywords:
Cardiovascular diseases, diabetes mellitus, prediction, machine learning, artificial intelligenceAbstract
Cardiovascular diseases (CVD) are one of the most common causes of mortality on the planet, and early prediction of the development of their complications is one of the most difficult tasks in medicine in recent years. Currently, CVD forecasting is proposed using various machine learning algorithms such as logistic regression, naive Bayes method, support vector machine, random forest, extreme gradient, etc. Using these methods, predict the likelihood of developing CVD and its complications.
This review, carried out according to the rules of the PRISMA meta-analysis, describes AI models for predicting CVD in adults with type 2 diabetes mellitus (T2DM), which were conducted on cohorts of adult patients with T2DM, predicting the risk of developing CVD in patients with T2DM (over a certain period of time) and developing the model artificial intelligence (machine learning or deep learning models).
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