Comparative Analysis of Logistic Regression, Gradient Boosted Trees, SVM, and Random Forest Algorithms for Prediction of Acute Kidney Injury Requiring Dialysis After Cardiac Surgery.
Evi Diana OmarHasnah MatAinil Zafirah Abd KarimRidwan SanaudiFairol H IbrahimMohd Azahadi OmarMuhd Zulfadli Hafiz IsmailVivek Jason JayarajBak Leong GohPublished in: International journal of nephrology and renovascular disease (2024)
These findings imply that Gradient Boosted Trees and Random Forest might be an effective method for identifying patients who would develop AKI following heart surgery. However specific goals, sensitivity/specificity trade-offs, and consideration of the practical ramifications should all be considered when choosing an algorithm.
Keyphrases
- acute kidney injury
- climate change
- machine learning
- cardiac surgery
- deep learning
- minimally invasive
- neural network
- coronary artery bypass
- chronic kidney disease
- heart failure
- end stage renal disease
- atrial fibrillation
- surgical site infection
- percutaneous coronary intervention
- peritoneal dialysis
- structural basis
- acute coronary syndrome