Unsupervised Learning Applied to the Stratification of Preterm Birth Risk in Brazil with Socioeconomic Data

Preterm birth (PTB) is a phenomenon that brings risks and challenges for the survival of the newborn child. Despite many advances in research, not all the causes of PTB are already clear. It is understood that PTB risk is multi-factorial and can also be associated with socioeconomic factors. Thereby...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	International journal of environmental research and public health 2022-05, Vol.19 (9), p.5596
Hauptverfasser:	Lopes, Jr, Márcio L B, Barbosa, Raquel de M, Fernandes, Marcelo A C
Format:	Artikel
Sprache:	eng
Schlagworte:	Birth Brazil - epidemiology Clustering Datasets Education Federal government Female Health risks Humans Infant, Newborn Learning Machine learning Mothers Municipalities Neighborhoods Pregnancy Premature birth Premature Birth - epidemiology Premature Birth - etiology Principal components analysis Quality of Life Risk Risk Factors Sanitation Social factors Socioeconomic data Socioeconomic Factors Socioeconomics Statistical methods Unsupervised learning Unsupervised Machine Learning Working conditions
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!