Predicting acute kidney injury following open partial nephrectomy treatment using SAT-pruned explainable machine learning model.

ABSTRACT:

Background

One of the most prevalent complications of Partial Nephrectomy (PN) is Acute Kidney Injury (AKI), which could have a negative impact on subsequent renal function and occurs in up to 24.3% of patients undergoing PN. The aim of this study was to predict the occurrence of AKI following PN using preoperative parameters by applying machine learning algorithms.

Methods

We included all adult patients (n = 723) 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

SUBMITTER: Lazebnik T 

PROVIDER: S-EPMC9112450 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature