Construction of prognostic models is a promising direction for preventive medicine. The search for new factors affecting cardiovascular health is an important addition to conventional risk scores. The aim of the study was to search for significant cardiovascular risk factors and develop a prognostic model using machine learning in healthy individuals. The analysis was based on an dataset of anamnestic, clinical, paraclinical, socio-economic and other parameters of two stages of the epidemiological study (Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo), which included 1 217 participants aged 35–70 years. There were 70.9 % (n = 863) and 29.1 % (n = 35) healthy respondents and cardiovascular patients, respectively. A total of 1, 915 features were analyzed using artificial intelligence. We identified 28 significant predictors of the following unfavorable cardiovascular outcomes: angina, myocardial infarction, heart failure, stroke, arrhythmias (atrial fibrillation and/or flutter), etc. Based on these, a prognostic model was developed. It should be noted that the most significant parameters included the forced expiratory volume in one second, internal fat proportion, no alcohol consumption, a change in salt intake after a doctor's recommendation, and no job. The paper determined the significant features that had not previously been recognized as cardiovascular risk factors affecting cardiovascular health. This undoubtedly provides an information gain for conventional prognostic models.
Construction of prognostic models is a promising direction for preventive medicine. The search for new factors affecting cardiovascular health is an important addition to conventional risk scores. The aim of the study was to search for significant cardiovascular risk factors and develop a prognostic model using machine learning in healthy individuals. The analysis was based on an dataset of anamnestic, clinical, paraclinical, socio-economic and other parameters of two stages of the epidemiological study (Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo), which included 1 217 participants aged 35–70 years. There were 70.9 % (n = 863) and 29.1 % (n = 35) healthy respondents and cardiovascular patients, respectively. A total of 1, 915 features were analyzed using artificial intelligence. We identified 28 significant predictors of the following unfavorable cardiovascular outcomes: angina, myocardial infarction, heart failure, stroke, arrhythmias (atrial fibrillation and/or flutter), etc. Based on these, a prognostic model was developed. It should be noted that the most significant parameters included the forced expiratory volume in one second, internal fat proportion, no alcohol consumption, a change in salt intake after a doctor's recommendation, and no job. The paper determined the significant features that had not previously been recognized as cardiovascular risk factors affecting cardiovascular health. This undoubtedly provides an information gain for conventional prognostic models.
The study was supported by the Russian Federation, specifically the Ministry of Science and Higher Education of the Russian Federation, under the Agreement for providing grant funding in the form of subsidies from the federal budget, dated September 30, 2022, No. 075-15-2022-1202. The study is a part of a comprehensive scientific and technological program of the full innovation cycle, entitled “Development and implementation of technologies in the fields of solid mineral exploration and extraction, industrial safety, bioremediation, and the creation of new products through deep coal processing, all with a gradual reduction of environmental impact and risks to the population`s well-being”. This initiative was established by the Russian Government`s decree No. 1144-r on May 11, 2022.