Innovations in early detection of chronic non-communicable diseases among adolescents through an easy-to-Use AutoML paradigm

In this research, we present an interpretable AutoML approach for the early diagnosis of hypertension and hyperinsulinemia among adolescents, conditions that are critical to identify during these formative years due to their requirement for lifelong care and monitoring. The dataset, collected from 2...

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Veröffentlicht in:	Health care management science Jg. 28; H. 3; S. 434 - 460
Hauptverfasser:	Rankovic, Nevena, Rankovic, Dragica, Lukic, Igor
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.09.2025 Springer Nature B.V
Schlagworte:	Accuracy Adolescence Adolescent Business and Management Child development Chronic Disease Chronic illnesses Cross-Sectional Studies Deep learning Diabetes Disease Early Diagnosis Econometrics Epidemics Female Health Administration Health care industry Health Informatics Humans Hypertension Hypertension - diagnosis Insulin resistance Lifestyles Machine learning Male Management Metabolic syndrome Neural networks Noncommunicable Diseases - diagnosis Operations Research/Decision Theory Patients Regression analysis Serbia Support vector machines Teenagers Serbia AutoML paradigm Tabular variational autoencoder (TVAE) synthetic generation Model-agnostic/post-hoc methods Chronic non-communicable diseases
ISSN:	1386-9620, 1572-9389, 1572-9389
Online-Zugang:	Volltext
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