Predicting Elder Abuse Using a Random Forest Classifier.

This paper aims to identify key factors influencing elder abuse within the family and to further explore the heterogeneity of these factors across different types of elder abuse. The data were drawn from the China Longitudinal Aging Social Survey and the final valid sample was 10,703. A random fores...

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Detalles Bibliográficos

Publicado en:	Journal of Interpersonal Violence Vol. 41; no. 15/16; pp. 5415 - 5435
Autores principales:	Zeng, Qiyan, Wang, Yannan, Zhao, Fuming, He, Zhipeng
Formato:	Artículo
Publicado:	Sage Publications Inc. Aug2026
Materias:	China Community health services Home care services Abuse of older people Health status indicators Economic status Intergenerational relations Random forest algorithms Prediction models Research funding Statistical sampling Machine learning Algorithms Sensitivity & specificity (Statistics)
Acceso en línea:	Ver este registro en EBSCOhost