The Kabardino-Balkarian State University named after H. M. Berbekov has created an intelligent system capable of determining the risk of developing heart failure in advance.
The machine learning algorithm is built on processing a large array of anonymized medical data from open sources. The training sample included the results of laboratory tests, clinical indicators, data from medical history, as well as information about patients' daily habits. This multi-layered approach helps the system identify hidden relationships and predict the possible development of the disease with high accuracy.
The technology acts as a specialist assistant. It provides an objective analytical assessment, based on which the doctor can make an informed decision about further examination, prescribing treatment, or changing the patient's lifestyle. Machine learning algorithms record subtle dependencies in information that often go unnoticed during standard diagnostics, and identify underestimated risk factors.
The development provides cardiologists with new tools for disease prevention and personalized treatment approaches. In addition to clinical benefits, the system improves the efficiency of medical institutions: it helps reduce the burden on hospitals and saves time through rapid information processing and structured analysis.
Specialists call the ability to predict the disease before the appearance of pronounced symptoms a key advantage of the technology. This allows for the development of an individual monitoring and treatment strategy in advance, reducing the likelihood of complications and the need for emergency hospitalization.
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