Article(id=1320388897763349049, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320388863101612371, articleNumber=null, orderNo=null, doi=10.11876/mimt202502006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792734844866, onlineDateStr=2026-10-23, pubDate=1744214400000, pubDateStr=2025-04-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792734844866, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792734844866, creator=13701087609, updateTime=1792734844866, updator=13701087609, issue=Issue{id=1320388863101612371, tenantId=1146029695717560320, journalId=1319373289681944729, year='2025', volume='31', issue='2', pageStart='1', pageEnd='96', issueExtLink='null', onlineDate='null', pubDate='1744214400000', pubDateStr='2025-04-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792734836602, creator='13701087609', updateTime=1792746450319, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320437574611620097, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320388863101612371, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320437574611620098, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320388863101612371, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=29, endPage=32, ext={EN=ArticleExt(id=1320388898136642106, articleId=1320388897763349049, tenantId=1146029695717560320, journalId=1319373289681944729, language=EN, title=Research on a machine learning-based fall risk assessment system for inpatients, columnId=null, journalTitle=Modern Instruments & Medical Treatment, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Fall risk assessment is of significant importance in the elderly population as falls constitute a primary cause of injuries and fatalities among the elderly. This paper comprehensively outlines the commonly used algorithms and classification features in fall risk assessment tools constructed using machine learning techniques. By comparing the performance of these models on real-world datasets, it delves into the strengths and weaknesses of various algorithms and their applicable scenarios. The article provides detailed insights into the data prepossessing steps and model construction processes commonly employed in each model, offering a comprehensive comparison. In conclusion, the paper summarizes the current shortcomings in research and anticipates potential directions for future developments in this field.

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