Improvement of cardiovascular risk assessment using machine learning methods
The increase in the prevalence of cardiovascular diseases (CVDs) specifies the importance of their prediction, the need for accurate risk stratification, preventive and treatment interventions. Large medical databases and technologies for their processing in the form of machine learning algorithms t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | Russian |
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«FIRMA «SILICEA» LLC
2022-01-01
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| Series: | Российский кардиологический журнал |
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| Online Access: | https://russjcardiol.elpub.ru/jour/article/view/4618 |
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| author | A. V. Gusev D. V. Gavrilov R. E. Novitsky T. Yu. Kuznetsova S. A. Boytsov |
| author_facet | A. V. Gusev D. V. Gavrilov R. E. Novitsky T. Yu. Kuznetsova S. A. Boytsov |
| author_sort | A. V. Gusev |
| collection | DOAJ |
| description | The increase in the prevalence of cardiovascular diseases (CVDs) specifies the importance of their prediction, the need for accurate risk stratification, preventive and treatment interventions. Large medical databases and technologies for their processing in the form of machine learning algorithms that have appeared in recent years have the potential to improve predictive accuracy and personalize treatment approaches to CVDs. The review examines the application of machine learning in predicting and identifying cardiovascular events. The role of this technology both in the calculation of total cardiovascular risk and in the prediction of individual diseases and events is discussed. We compared the predictive accuracy of current risk scores and various machine learning algorithms. The conditions for using machine learning and developing personalized tactics for managing patients with CVDs are analyzed. |
| format | Article |
| id | doaj-art-9f4d49d61c2b4a24aadeb1b31f470531 |
| institution | DOAJ |
| issn | 1560-4071 2618-7620 |
| language | Russian |
| publishDate | 2022-01-01 |
| publisher | «FIRMA «SILICEA» LLC |
| record_format | Article |
| series | Российский кардиологический журнал |
| spelling | doaj-art-9f4d49d61c2b4a24aadeb1b31f4705312025-08-20T02:59:04Zrus«FIRMA «SILICEA» LLCРоссийский кардиологический журнал1560-40712618-76202022-01-01261210.15829/1560-4071-2021-46183408Improvement of cardiovascular risk assessment using machine learning methodsA. V. Gusev0D. V. Gavrilov1R. E. Novitsky2T. Yu. Kuznetsova3S. A. Boytsov4K-SkaiK-SkaiK-SkaiPetrozavodsk State UniversityNational Medical Research Center of CardiologyThe increase in the prevalence of cardiovascular diseases (CVDs) specifies the importance of their prediction, the need for accurate risk stratification, preventive and treatment interventions. Large medical databases and technologies for their processing in the form of machine learning algorithms that have appeared in recent years have the potential to improve predictive accuracy and personalize treatment approaches to CVDs. The review examines the application of machine learning in predicting and identifying cardiovascular events. The role of this technology both in the calculation of total cardiovascular risk and in the prediction of individual diseases and events is discussed. We compared the predictive accuracy of current risk scores and various machine learning algorithms. The conditions for using machine learning and developing personalized tactics for managing patients with CVDs are analyzed.https://russjcardiol.elpub.ru/jour/article/view/4618cardiovascular diseasesrisk assessmentprediction of cardiovascular eventsmachine learningartificial intelligence |
| spellingShingle | A. V. Gusev D. V. Gavrilov R. E. Novitsky T. Yu. Kuznetsova S. A. Boytsov Improvement of cardiovascular risk assessment using machine learning methods Российский кардиологический журнал cardiovascular diseases risk assessment prediction of cardiovascular events machine learning artificial intelligence |
| title | Improvement of cardiovascular risk assessment using machine learning methods |
| title_full | Improvement of cardiovascular risk assessment using machine learning methods |
| title_fullStr | Improvement of cardiovascular risk assessment using machine learning methods |
| title_full_unstemmed | Improvement of cardiovascular risk assessment using machine learning methods |
| title_short | Improvement of cardiovascular risk assessment using machine learning methods |
| title_sort | improvement of cardiovascular risk assessment using machine learning methods |
| topic | cardiovascular diseases risk assessment prediction of cardiovascular events machine learning artificial intelligence |
| url | https://russjcardiol.elpub.ru/jour/article/view/4618 |
| work_keys_str_mv | AT avgusev improvementofcardiovascularriskassessmentusingmachinelearningmethods AT dvgavrilov improvementofcardiovascularriskassessmentusingmachinelearningmethods AT renovitsky improvementofcardiovascularriskassessmentusingmachinelearningmethods AT tyukuznetsova improvementofcardiovascularriskassessmentusingmachinelearningmethods AT saboytsov improvementofcardiovascularriskassessmentusingmachinelearningmethods |