Machine learning in predicting adverse cardiovascular events in patients with type 2 diabetes. Opinion on the problem

This paper analyzes the prospects for using machine learning to assess cardiovascular risk in patients with type 2 diabetes. It is shown that modern algorithms, particularly neural networks, demonstrate high efficiency in risk prediction. However, despite the high accuracy of the models, their implementation is limited by several issues, such as the lack of external validation on independent samples and the difficulty of integrating them into clinical decision support systems. The authors emphasize the need for standardized study design and transparency in algorithm performance to implement machine learning in widespread clinical practice.

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