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The aim of this study was to predict the occurrence of AKI following PN using preoperative parameters by applying machine learning algorithms.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We included all adult patients (n\u2009=\u2009723) who underwent open PN in our department since 1995 and on whom we have data on the pre-operative renal function. We developed a random forest (RF) model with Boolean satisfaction-based pruned decision trees for binary classification (AKI or non-AKI). Hyper-parameter grid search was performed to optimize the model's performance. Fivefold cross-validation was applied to evaluate the model. We implemented a RF model with greedy feature selection to binary classify AKI and non-AKI cases based on pre-operative data.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The best model obtained a 0.69 precision and 0.69 recall in classifying the AKI and non-AKI groups on average (k\u2009=\u20095). In addition, the model's probability to correctly classify a new prediction is 0.75. The proposed model is available as an online calculator.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our model predicts the occurrence of AKI following open PN with (75%) accuracy. We plan to externally validate this model and modify it to minimally-invasive PN.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-01877-8","type":"journal-article","created":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T13:25:08Z","timestamp":1652707508000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Predicting acute kidney injury following open partial nephrectomy treatment using SAT-pruned explainable machine learning model"],"prefix":"10.1186","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7851-8147","authenticated-orcid":false,"given":"Teddy","family":"Lazebnik","sequence":"first","affiliation":[]},{"given":"Zaher","family":"Bahouth","sequence":"additional","affiliation":[]},{"given":"Svetlana","family":"Bunimovich-Mendrazitsky","sequence":"additional","affiliation":[]},{"given":"Sarel","family":"Halachmi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,16]]},"reference":[{"key":"1877_CR1","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.ejca.2018.07.005","volume":"103","author":"J Ferlay","year":"2018","unstructured":"Ferlay J, Colombet M, Soerjomataram I, et al. 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