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While multiple imputation by chained equations (MICE) is a widely used method, its sequential nature introduces uncertainty, potentially impacting the prediction model performance. We proposed and evaluated three uncertainty-aware functions (i.e., uncertainty sampling (US), probability of improvement (PI), and expected improvement (EI)) integrated with linear regression (LinearReg), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) using three large datasets: chronic kidney disease (CKD, n\u2009=\u200931,043), hypertension cohort from Ramathibodi Hospital (HT-RAMA, n\u2009=\u2009140,047) and Khon Kaen University Hospital (HT-KKU, n\u2009=\u2009108,942) with high missing rates. In the CKD cohort, uncertainty-aware models significantly improved performance (evaluated by root mean squared error (RMSE) and mean absolute error (MAE)) over standard MICE, except for XGBoost. LinearReg-EI performed best (RMSE 0.12, MAE 0.36), followed by RF-EI (RMSE 0.22, MAE 0.34), and DT-EI (RMSE 0.21, MAE 0.38). In HT-RAMA, LinearReg-US performed best (RMSE 0.24, MAE 8.15), outperforming RF-US (RMSE 0.92, MAE 8.58) and DT-PI (RMSE 0.96, MAE 8.74). Similarly, in HT-KKU, LinearReg-US performed best (RMSE 0.98, MAE 12.00), followed by RF-PI (RMSE 1.93, MAE 12.90) and DT-US (RMSE 2.10, MAE 12.63). Uncertainty-aware models produced imputed distributions closely resembling the original data, unlike standard MICE. Our findings suggest that incorporating uncertainty functions can improve MICE, particularly for LinearReg, RF and DT. Further research is warranted to validate these findings across diverse clinical settings and model types.<\/jats:p>","DOI":"10.1186\/s40537-025-01136-3","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T15:23:21Z","timestamp":1744903401000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Uncertainty-aware approach for multiple imputation using conventional and machine learning models: a real-world data study"],"prefix":"10.1186","volume":"12","author":[{"given":"Romen Samuel","family":"Wabina","sequence":"first","affiliation":[]},{"given":"Panu","family":"Looareesuwan","sequence":"additional","affiliation":[]},{"given":"Suphachoke","family":"Sonsilphong","sequence":"additional","affiliation":[]},{"given":"Htun","family":"Teza","sequence":"additional","affiliation":[]},{"given":"Wanchana","family":"Ponthongmak","sequence":"additional","affiliation":[]},{"given":"Gareth","family":"McKay","sequence":"additional","affiliation":[]},{"given":"John","family":"Attia","sequence":"additional","affiliation":[]},{"given":"Anuchate","family":"Pattanateepapon","sequence":"additional","affiliation":[]},{"given":"Anupol","family":"Panitchote","sequence":"additional","affiliation":[]},{"given":"Ammarin","family":"Thakkinstian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"issue":"14","key":"1136_CR1","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.1056\/NEJMsr1203730","volume":"367","author":"RJ Little","year":"2012","unstructured":"Little RJ, D\u2019Agostino R, Cohen M, Dickersin K, Scott E, Farrar J, et al. 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No. MURA2024\/468 for CKD, COA No. MURA2023\/689 for HT-RAMA; and HE681020 for HT-KKU. Due to a retrospective study design, the informed consents were waived by the Ethics Committee.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"95"}}