Peer reviewed
Direct linkERIC Number: EJ1344897
Record Type: Journal
Publication Date: 2022
Pages: 20
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0748-1756
EISSN: EISSN-1947-6302
Available Date: N/A
Improving Predictive Classification Models Using Generative Adversarial Networks in the Prediction of Suicide Attempts
Mangino, Anthony A.; Smith, Kendall A.; Finch, W. Holmes; Hernández-Finch, Maria E.
Measurement and Evaluation in Counseling and Development, v55 n2 p116-135 2022
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.
Descriptors: Prediction, Suicide, Artificial Intelligence, Networks, Models, Classification, Regression (Statistics), Bayesian Statistics, Health Behavior, National Surveys, Risk, High School Students, Accuracy, Outcome Measures, Measurement
Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Publication Type: Journal Articles; Reports - Research
Education Level: High Schools; Secondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Assessments and Surveys: Youth Risk Behavior Survey
Grant or Contract Numbers: N/A
Author Affiliations: N/A
