CHRONIC KIDNEY DISEASE AND THE CREATION OF PREDICTIVE  MATHEMATICAL MODELS

dc.contributor.author	ABBASOV A.K., QOBILJONOV J.Q
dc.date.accessioned	2026-04-21T15:33:09Z
dc.date.available	2026-04-21T15:33:09Z
dc.date.issued	2025-11
dc.identifier.uri	http://repo.tma.uz/xmlui/handle/1/3804
dc.description.abstract	Random forests are somewhat interpretable, as they return feature values that can be used to compare the most useful variables for making predictions. Random forests are generally very accurate and perform well on nonlinear problems with many features, as they perform implicit feature selection.	en_US
dc.language.iso	en_US	en_US
dc.publisher	O'zbekiston, Toshkent "O'zbekiston harbiy tibbiyoti jurnali"	en_US
dc.subject	Random Forest Regression, mathematical models, chronic kidney disease.	en_US
dc.title	CHRONIC KIDNEY DISEASE AND THE CREATION OF PREDICTIVE MATHEMATICAL MODELS	en_US
dc.type	Article	en_US