Prediction and Assessment of Myocardial Infarction Risk on the Base of Medical Report Text Collection

Introduction. Myocardial infarction remains one of the leading causes of death worldwide, resulting from sudden disruption of blood supply to the heart muscle. Key risk factors include smoking, age, gender, high cholesterol levels, diabetes, and others. Despite advancements in diagnostics and treatment, early detection of heart attack risk is crucial for reducing mortality and improving patient quality of life. This paper explores an approach to predicting heart attack risk based on analysis of text data of medical reports using machine learning. The purpose of the article is to demonstrate how the application of machine learning, particularly the Naive Bayes classifier, can enhance the prediction of myocardial infarction risk through the analysis of extensive medical data. By leveraging a depersonalized database from SSO CITHC SAA, containing medical records collected during a decade of operating, this study seeks to reveal how the identification of critical patterns and factors can improve prediction accuracy. Additionally, the article explores how integrating these predictive models into clinical decision support systems can refine medical diagnostics and decision-making processes. Results. The proposed prediction model demonstrated high efficiency in identifying patients at increased risk of heart attack. By analyzing the frequency of specific words in medical records, the algorithm successfully predicted a high risk of heart attack for 80 % of patients with an expected event. This underscores the significant potential of leveraging textual data and machine learning methods in medical diagnostics. Moreover, the reduction in false predictions highlights the model's reliability and suitability for practical application. Conclusions. Employing machine learning for heart attack risk prediction based on medical data analysis represents a promising direction in modern medicine. The developed model showcases the possibility of enhancing diagnostic and predictive accuracy, which can substantially influence treatment strategy decisions and improve patient outcomes. Integrating such tools into clinical practice will facilitate more informed decisions by physicians and reduce patient risks. Keywords: myocardial infarction, risk prediction, machine learning, database, Naive Bayes classifier, medical analytics.