Читать книгу Predicting Heart Failure - Группа авторов - Страница 32

Invasive and non-invasive methods offer a wealth of diagnostic information. However, the interpretation of the available information can only be possible with the help of a physician. The increase in heart patients and the increase in patient data in parallel make it difficult to evaluate the data and extract information from them day by day. The intersection of the symptoms of heart disease with the symptoms of other diseases also makes the diagnosis of the disease a difficult problem. For this reason, there is a need to evaluate the data obtained with the help of invasive and non-invasive techniques with intelligent analysis tools in order to increase the diagnostic accuracy. Artificial intelligence and machine learning will assist physicians in intelligent analysis. Machine learning models trained with past patient data can be used to diagnose future cases. With the diagnostic capabilities to be gained by the machines, it will sometimes be possible for them to diagnose more precisely and more sensitively than the physicians. Supporting decision support systems working in expert systems logic with machine learning models will enable them to give better results.