ArticleInternational journal of nephrology and renovascular disease2024
Comparative Analysis of Logistic Regression, Gradient Boosted Trees, SVM, and Random Forest Algorithms for Prediction of Acute Kidney Injury Requiring Dialysis After Cardiac Surgery.
Article in International journal of nephrology and renovascular disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
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- Detection of disturbances and cyber-attacks in smart grids using explainable machine learning.Scientific reports · 2026Article
- Interpretable multimodal machine learning for diagnosis of drug-resistant tuberculosis.Frontiers in digital health · 2026Article
- An Immune Model Incorporating NK Cells and IL-12 to Predict the Efficacy of the Initial Treatment Cycle of Efgartigimod in Generalized Myasthenia Gravis.Drug design, development and therapy · 2026Observational
- Antimicrobial Peptides Against Antimicrobial-Resistant Bacteria: Focus on Machine Learning.Infection and drug resistance · 2026Review
- Observational
- Cheminformatics and Machine Learning-Driven QSAR Analysis of SPHK2 Inhibitors for Anticancer Drug Design.Current medicinal chemistry · 2026Article
- Key predictors of postpartum depression and anxiety symptoms among mothers in Kilifi, Kenya: a machine learning approach.Frontiers in psychiatry · 2026Article
- Machine learning-supported framework for the classification of mpox infection and MVA immunization from multiplexed serology data.Nature communications · 2025Article
- Development and external validation of a machine learning model to predict high flow nasal cannula failure.BMJ open respiratory research · 2025Article
- Application of artificial intelligence in predicting the results of open-heart surgery: a scoping review.BMC medical informatics and decision making · 2025Article
- Development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Evaluating machine learning approaches for host prediction using H3 influenza genomic data.PloS one · 2025Article
- Interpretable machine learning models for predicting short-term prognosis in AChR-Ab+ generalized myasthenia gravis using clinical features and systemic inflammation index.Frontiers in neurology · 2024Article
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9 authors.
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Abstract
Purpose: This study aimed to identify the best-performing algorithm for predicting Acute Kidney Injury (AKI) necessitating dialysis following cardiac surgery. Patients and Methods: The dataset encompassed patient data from a tertiary cardiothoracic center in Malaysia between 2011 and 2015, sourced from electronic health records. Extensive preprocessing and feature selection ensured data quality and relevance. Four machine learning algorithms were applied: Logistic Regression, Gradient Boosted Trees, Support Vector Machine, and Random Forest. The dataset was split into training and validation sets and the hyperparameters were tuned. Accuracy, Area Under the ROC Curve (AUC), precision, F-measure, sensitivity, and specificity were some of the evaluation criteria. Ethical guidelines for data use and patient privacy were rigorously followed throughout the study. Results: With the highest accuracy (88.66%), AUC (94.61%), and sensitivity (91.30%), Gradient Boosted Trees emerged as the top performance. Random Forest displayed strong AUC (94.78%) and accuracy (87.39%). In contrast, the Support Vector Machine showed higher sensitivity (98.57%) with lower specificity (59.55%), but lower accuracy (79.02%) and precision (70.81%). Sensitivity (87.70%) and specificity (87.05%) were maintained in balance via Logistic Regression. Conclusion: 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.
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