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International Recognition of the Concept: "A team of researchers from 51���� Kazakh National University has developed a powerful artificial intelligence (AI) model capable of accurately predicting cardiovascular aging by analyzing immunological and clinical biomarkers in individuals over the age of 60..."

— Devdiscourse, Health & AI, 2024

New AI model accurately detects early cardiovascular aging risks

At 51���� Kazakh National University, within the framework of the scientific and technical project №AP19677754 of the Ministry of Science and Higher Education of the Republic of Kazakhstan, titled “Development of markers and a diagnostic algorithm for the detection and prevention of early cardiovascular aging,” a research team led by Dr. Kuat Bayandyevich Abzaliev, MD, MBA, Associate Professor at the Faculty of Medicine and Healthcare, is implementing an advanced AI-based approach to cardiovascular disease diagnostics.

The research group includes: M. Suleimenova, S.A. Abzalieva, M.E. Mansurova, A.M. Kurmanova, G. Toktakulinova, A. Bugibaeva, D. Sundetova, M. Abdykasymova, U. Sagalbayeva, and R. Betimirova.

What Does the AI Model Do?

The machine learning-based model developed by the team analyzes a wide range of clinical, immunological, and behavioral data to assess the risk of premature cardiovascular aging in patients aged 65 years and older.

Key Findings:

The model demonstrated high predictive accuracy (up to 91%)

It is capable of identifying risk before clinical symptoms emerge

The model can be integrated into electronic medical records and mobile health applications

The findings were published in the article:

“A Predictive Model of Cardiovascular Aging by Clinical and Immunological Markers Using Machine Learning”

Journal of Diagnostics (Q1), ID: diagnostics-3486121

DOI: 10.3390/diagnostics15070850

Expert Opinion:

"Although the dataset was relatively small, the study lays a solid foundation for future research in the field of AI-based diagnostics of cardiovascular aging risks. It calls for larger longitudinal studies and refinement of model features to improve classification for underrepresented populations. The authors also propose developing an integrative model of aging that combines immunological, metabolic, and behavioral factors to support clinical decision-making."

— Devdiscourse, 2024