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	<title>FINDRISC &#8211; BIOENGINEER.ORG</title>
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		<title>Predicting the incidence of type 2 diabetes using FINDRISC and machine learning approaches: evidence from a population-based cohort</title>
		<link>https://bioengineer.org/predicting-the-incidence-of-type-2-diabetes-using-findrisc-and-machine-learning-approaches-evidence-from-a-population-based-cohort/</link>
		
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		<pubDate>Sat, 24 Oct 2026 07:48:48 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[AUC-ROC]]></category>
		<category><![CDATA[FINDRISC]]></category>
		<category><![CDATA[incidence]]></category>
		<category><![CDATA[logistic regression]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[population-based cohort]]></category>
		<category><![CDATA[Predicting]]></category>
		<category><![CDATA[Random Forest]]></category>
		<category><![CDATA[risk prediction]]></category>
		<category><![CDATA[screening]]></category>
		<category><![CDATA[type]]></category>
		<category><![CDATA[Type 2 diabetes]]></category>
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					<description><![CDATA[In a population-based cohort of 8,859 adults followed for six years, component-level machine learning models showed higher observed AUCs than the FINDRISC score for predicting incident type 2 diabetes, but the authors conclude the standard score remains the practical screening tool.]]></description>
		
		
		
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