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...
| Publicado en: | Journal of Interpersonal Violence Vol. 41; no. 15/16; pp. 5415 - 5435 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
Aug2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=195361953&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 195361953 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 08862605 JIV jtl: Journal of Interpersonal Violence issn: 08862605 maglogo: Y pubinfo: dt: Aug2026 vid: 41 iid: 15/16 pid: 344 pub: Sage Publications Inc. artinfo: ui: 195361953 10.1177/08862605251355973 ppf: 5415 ppct: 20 formats: tig: atl: Predicting Elder Abuse Using a Random Forest Classifier. aug: au: Zeng, Qiyan Wang, Yannan Zhao, Fuming He, Zhipeng affil: Research Academy for Rural Revitalization of Zhejiang Province, Zhejiang A&F University, Hangzhou City, China Jiyang College, Zhejiang A&F University, Zhuji City, China College of Economics and Management, China Agricultural University, Beijing, China su: 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) sug: subj: Community health services Home care services Abuse of older people Health status indicators Economic status Intergenerational relations China All Other Outpatient Care Centers Residential Mental Health and Substance Abuse Facilities Community health centres Other Individual and Family Services Other local, municipal and regional public administration Services for the Elderly and Persons with Disabilities Home Health Care Services Offices of all other health practitioners Offices of All Other Miscellaneous Health Practitioners Marketing Research and Public Opinion Polling Random forest algorithms Prediction models Research funding Statistical sampling Machine learning Algorithms Sensitivity & specificity (Statistics) keyword: elder abuse machine learning older people random forest elder abuse machine learning older people random forest ab: 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 forest classifier, a supervised machine learning method, was employed to identify the key influencing factors of elder abuse. Children's economic status is found to be the most important factor in predicting elder abuse, followed by the number of children, the health status of older people, intergenerational relations, children's time pressure, and the provision of home-based elderly care services. The likelihood of elder abuse decreases continuously with better economic status and less time pressure of children, better health of older people, more children, and better intergenerational relationships, whereas the influences of home-based elderly care services on elder abuse are not monotonous. Number of children contributes most to predict financial abuse, while children's economic status plays the most significant role in predicting physical and psychological abuse and neglect. This study is the first to apply a supervised machine learning approach with a random forest classifier for the identification of risk factors associated with elder abuse. The findings highlight the advantages of machine learning techniques in improving the prediction accuracy of elder abuse compared to traditional econometric models. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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