ArticleBMC medical informatics and decision making2026
Predicting anemia treatment outcomes in maintenance hemodialysis patients using multiple machine learning models.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundBased on machine learning prediction models, we explored the anemia treatment attainment of patients on maintenance hemodialysis (MHD) and identified important factors for personalized treatment of patients.
methodsWe collected clinical data from 222 patients on MHD at West China Fourth Hospital, Sichuan University. Multiple machine learning models were applied to analyze the predictors of renal anemia treatment outcomes. A comprehensive evaluation was made in terms of discrimination, calibration and clinical utility. The top five important predictors were weighted according to the importance ranking of the variables for each model.
resultsAmong the seven prediction models constructed in this study, the support vector classification model showed relatively high specificity (0.914). Logistic regression achieved the highest AUC (0.713), while stacking performed better in terms of precision (0.871) and recall (0.710) with a low Brier score (0.087). The composite results of the seven models suggested that albumin, total cholesterol, transferrin saturation, high-density lipoprotein cholesterol, and C-reactive protein were the top five significant predictors of treatment outcomes in renal anemia.
conclusionsThe important predictive factors suggested by this study may provide useful reference information for guiding treatment of renal anemia, thus improving patient outcomes.
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