A machine learning analysis of predictors of future hypertension in a young population.

Journal: Minerva cardiology and angiology

PMID: 38804625

Abstract

BACKGROUND: Early diagnosis of hypertension (HT) is crucial for preventing end-organ damage. This study aims to identify the risk factors for future HT in young individuals through the application of machine learning (ML) models.

Authors

Ozge Turgay Yildirim

Department of Cardiology, Eskisehir City Hospital, Eskisehir, Türkiye - ozgeturgay@gmail.com.
Mehmet Ozgeyik

Department of Cardiology, Eskisehir City Hospital, Eskisehir, Türkiye.
Selim Yildirim

Department of Economics, Faculty of Economics and Administrative Sciences, Anadolu University, Eskisehir, Türkiye.
Basar Candemir

Department of Cardiology, Ankara University Faculty of Medicine, Ankara, Türkiye.

Keywords

Adolescent Adult Age Factors Blood Pressure Blood Pressure Monitoring, Ambulatory Cholesterol, HDL Early Diagnosis Female Follow-Up Studies Humans Hypertension Logistic Models Machine Learning Male Predictive Value of Tests Risk Factors Triglycerides Young Adult

External Resources

View on PubMed Access via DOI PubMed (38804625)

A machine learning analysis of predictors of future hypertension in a young population.

Abstract

Authors

Keywords

External Resources

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