Improving Predictive Classification Models Using Generative Adversarial Networks in the Prediction of Suicide Attempts

Autor/inn/en	Mangino, Anthony A.; Smith, Kendall A.; Finch, W. Holmes; Hernández-Finch, Maria E.
Titel	Improving Predictive Classification Models Using Generative Adversarial Networks in the Prediction of Suicide Attempts
Quelle	In: Measurement and Evaluation in Counseling and Development, 55 (2022) 2, S.116-135 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Mangino, Anthony A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0748-1756
DOI	10.1080/07481756.2021.1906156
Schlagwörter	Prediction; Suicide; Artificial Intelligence; Networks; Models; Classification; Regression (Statistics); Bayesian Statistics; Health Behavior; National Surveys; Risk; High School Students; Accuracy; Outcome Measures; Measurement; Youth Risk Behavior Survey + Suchen Sie Ihr Suchwort? Vorhersage; Selbstmord; Künstliche Intelligenz; Analogiemodell; Classification system; Klassifikation; Klassifikationssystem; Regression; Regressionsanalyse; Health behaviour; Gesundheitsverhalten; Risiko; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Messverfahren
Abstract	A number of machine learning methods can be employed in the prediction of suicide attempts. However, many models do not predict new cases well in cases with unbalanced data. The present study improved prediction of suicide attempts via the use of a generative adversarial network. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01