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Mishkin I.A.

National Medical Research Center for Therapy and Preventive Medicine;
Kireevsk Central District Hospital

Kontsevaya A.V.

National Medical Research Center for Therapy and Preventive Medicine

Gusev A.V.

Russian Research Institute of Health;
K-SkAI

Saharov A.A.

Renaissance Insurance Group

Drapkina O.M.

National Medical Research Center for Therapy and Preventive Medicine;
A.I. Yevdokimov Moscow State University of Medicine and Dentistry

Development and testing of new methodical approaches for predicting cardiovascular events in healthy people using machine learning technology based on the «INTEREPID» international research

Authors:

Mishkin I.A., Kontsevaya A.V., Gusev A.V., Saharov A.A., Drapkina O.M.

More about the authors

Journal: Russian Journal of Preventive Medicine. 2024;27(3): 72‑79

DOI: 10.17116/profmed20242703172

Read: 1680 times

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DEVELOPMENT AND TESTING OF NEW METHODICAL APPROACHES FOR PREDICTING CARDIOVASCULAR EVENTS IN HEALTHY PEOPLE USING MACHINE LEARNING TECHNOLOGY BASED ON THE «INTEREPID» INTERNATIONAL RESEARCH
DEVELOPMENT AND TESTING OF NEW METHODICAL APPROACHES FOR PREDICTING CARDIOVASCULAR EVENTS IN HEALTHY PEOPLE USING MACHINE LEARNING TECHNOLOGY BASED ON THE «INTEREPID» INTERNATIONAL RESEARCH

To cite this article:

Mishkin IA, Kontsevaya AV, Gusev AV, Saharov AA, Drapkina OM. Development and testing of new methodical approaches for predicting cardiovascular events in healthy people using machine learning technology based on the «INTEREPID» international research. Russian Journal of Preventive Medicine. 2024;27(3):72‑79. (In Russ.)
https://doi.org/10.17116/profmed20242703172

Подписка 2025-2 (Russian Journal of Preventive Medicine)
 
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Cardiovascular diseases remained the leading cause of death all over the world in 2023 yr. Prevention is a key issue for the development of current medicine to reduce the burden of this pathology. Up to date, main tools of monitoring are assessment scales of absolute and relative cardiovascular risk. However, more and more researchers consider the use of artificial intelligence in predicting heart diseases due to development of information technologies.

OBJECTIVE

To develop and test new methodical approaches for predicting cardiovascular events in healthy people using artificial intelligence.

MATERIAL AND METHODS

The study was performed on the basis of «INTEREPID» international research data. The sample consisted of 2.392 thous. participants who have been observed for 4 years, among them 1022 (42.7%) were men, 1369 (57.2%) — women. The analysis included 191 predictors. We used 5 classification algorithms to create prediction models on the Python programming environment: RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier, XGBClassifier, LGBMClassifier. ROC-analysis was used to evaluate the effectiveness of prediction models.

RESULTS

The most effective algorithm was GradientBoostingClassifier with AUC-0.76. The worst result had ExtraTreesClassifier with AUC-0.68. The most significant risk factors were age, C-reactive protein level in the blood and animal fat consumption.

CONCLUSION

As a result of the research we were able to obtain a prediction algorithm with relatively good quality of discrimination. Further studies on larger amount of data are necessary to improve this development.

Keywords:

cardiovascular risk
artificial intelligence
machine learning
cardiovascular diseases prognosis
diseases of circulatory system

Authors:

Mishkin I.A.

National Medical Research Center for Therapy and Preventive Medicine;
Kireevsk Central District Hospital

Kontsevaya A.V.

National Medical Research Center for Therapy and Preventive Medicine

Gusev A.V.

Russian Research Institute of Health;
K-SkAI

Saharov A.A.

Renaissance Insurance Group

Drapkina O.M.

National Medical Research Center for Therapy and Preventive Medicine;
A.I. Yevdokimov Moscow State University of Medicine and Dentistry

Received:

13.07.2023

Accepted:

21.09.2023

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