A Predictive Model of Cardiovascular Aging by Clinical and Immunological Markers Using Machine Learning DOI Creative Commons
Мадина Сулейменова, Kuat Аbzaliyev, Мадина Мансурова

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(7), P. 850 - 850

Published: March 27, 2025

Background/Objectives: Aging and immune mechanisms play a key role in the development of cardiovascular disease (CVD), especially context chronic inflammation. Therefore, order to detect early aging elderly, we have developed prognostic model based on clinical immunological markers using machine learning. Methods: This paper analyzes relationships between markers, parameters, lifestyle factors individuals over 60 years age. A learning (ML) including random forest, logistic regression, k-nearest neighbors, XGBoost was predict rate risk CVD. Correlation anal is revealed significant associations (CD14+, HLA-DR, IL-10, CD8+), parameters (BMI, coronary heart disease, hypertension, diabetes), behavioral (physical activity, smoking, alcohol). Results: The results study confirm that systemic inflammation, as reflected by such CD14+, plays central pathogenesis related diseases. CD14+ shows moderate positive correlation with post-infarction cardiosclerosis, accounting for 37%. HLA-DR correlates body mass index at 39%. negative association IL-10 level BMI also found, where reaches 52% (r = -0.52). CD8+ cells smoking their number, being 40%. Training performed data models were evaluated accuracy, ROC-AUC, F1-score metrics. Among all trained models, best, achieving an accuracy 91% area under ROC curve (AUC) 0.8333. Conclusions: reveals correlations which allows assessment individual risks premature aging. R (version 4.3.0) specialized libraries matrix construction visualization used analysis, Python 3.11.11) training.

Language: Английский

A Predictive Model of Cardiovascular Aging by Clinical and Immunological Markers Using Machine Learning DOI Creative Commons
Мадина Сулейменова, Kuat Аbzaliyev, Мадина Мансурова

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(7), P. 850 - 850

Published: March 27, 2025

Background/Objectives: Aging and immune mechanisms play a key role in the development of cardiovascular disease (CVD), especially context chronic inflammation. Therefore, order to detect early aging elderly, we have developed prognostic model based on clinical immunological markers using machine learning. Methods: This paper analyzes relationships between markers, parameters, lifestyle factors individuals over 60 years age. A learning (ML) including random forest, logistic regression, k-nearest neighbors, XGBoost was predict rate risk CVD. Correlation anal is revealed significant associations (CD14+, HLA-DR, IL-10, CD8+), parameters (BMI, coronary heart disease, hypertension, diabetes), behavioral (physical activity, smoking, alcohol). Results: The results study confirm that systemic inflammation, as reflected by such CD14+, plays central pathogenesis related diseases. CD14+ shows moderate positive correlation with post-infarction cardiosclerosis, accounting for 37%. HLA-DR correlates body mass index at 39%. negative association IL-10 level BMI also found, where reaches 52% (r = -0.52). CD8+ cells smoking their number, being 40%. Training performed data models were evaluated accuracy, ROC-AUC, F1-score metrics. Among all trained models, best, achieving an accuracy 91% area under ROC curve (AUC) 0.8333. Conclusions: reveals correlations which allows assessment individual risks premature aging. R (version 4.3.0) specialized libraries matrix construction visualization used analysis, Python 3.11.11) training.

Language: Английский

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