Evidence map›Paper›PMID 42452601›Full record

ArticleJournal of clinical medicine2026

Predicting the Risk of Cardiovascular Diseases in the Elderly Based on Clinical Data and Heart Rate Variability Using Machine Learning.

Kuat Abzaliyev, Akbota Bugibayeva, Symbat Abzaliyeva, Gulsim Akhmetova, Gulzira Balkanay, Aliya Omarbayeva, Saken Anartayev, Nazima Zarubekova, Madina Suleimenova

Abstract read
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In one paragraph

Article in Journal of clinical medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Kuat AbzaliyevDepartment of Big Data and Artificial Intelligence, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.
Akbota BugibayevaDepartment of Postgraduate Education, Kazakhstan's Medical University "Kazakhstan School of Public Health", Almaty 050040, Kazakhstan.ORCID 0000-0003-1417-2711
Symbat AbzaliyevaDepartment of Big Data and Artificial Intelligence, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.ORCID 0000-0002-2618-1298
Gulsim AkhmetovaDepartment of Emergency and Urgent Medical Care, S.D. Asfendiyarov Kazakh National Medical University, Almaty 050040, Kazakhstan.
Gulzira BalkanayDepartment of Emergency and Urgent Medical Care, S.D. Asfendiyarov Kazakh National Medical University, Almaty 050040, Kazakhstan.
Aliya OmarbayevaDepartment of Internal Medicine, Faculty of Medicine and Health Care, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.
Saken AnartayevDepartment of Interventional Cardiology, Arrhythmology and Angiosurgery, City Clinical Hospital 7, Almaty 050006, Kazakhstan.
Nazima ZarubekovaDepartment of Emergency and Urgent Medical Care, S.D. Asfendiyarov Kazakh National Medical University, Almaty 050040, Kazakhstan.
Madina SuleimenovaDepartment of Information Systems, International Information Technology University, Almaty 050040, Kazakhstan.ORCID 0009-0003-8553-5353

Funding

the Committee of Science and Higher Education of the Ministry of Science and Higher Education of the Republic of Kazakhstan No. AP19677754
6 · The paper itself

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality in the elderly worldwide. Over the past two decades, there has been a wealth of evidence of a close relationship between autonomic nervous system activity and cardiovascular mortality, including sudden cardiac death. Heart rate variability (HRV), derived from photoplethysmographic (PPG) signals, is increasingly recognized as a promising non-invasive digital marker for evaluating autonomic nervous system function and stratifying CVD risk. The application of machine learning algorithms to PPG-derived HRV analysis offers a promising approach for improving CVD risk stratification and facilitating the development of personalized medicine strategies.

Indexed as

cardiovascular diseaseheart rate variabilitymachine learningphotoplethysmographypredicting

Identifiers

PMID42452601
PMCPMC13362802

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.