Comparative Analysis of Logistic Regression, Gradient Boosted Trees, SVM, and Random Forest Algorithms for Prediction of Acute Kidney Injury Requiring Dialysis After Cardiac Surgery

Abstract

Evi Diana Omar,1 Hasnah Mat,1 Ainil Zafirah Abd Karim,1 Ridwan Sanaudi,1 Fairol H Ibrahim,2 Mohd Azahadi Omar,1 Muhd Zulfadli Hafiz Ismail,1 Vivek Jason Jayaraj,1 Bak Leong Goh2 1Sector for Biostatistics and Data Repository, National Institutes of Health, Ministry of Health Malaysia, Shah Alam, Selangor, Malaysia; 2Hospital Sultan Idris Shah Serdang, Ministry of Health Malaysia, Kajang, Selangor, MalaysiaCorrespondence: Ainil Zafirah Abd Karim, Email [email protected]: 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.Keywords: acute kidney injury, cardiac surgery, machine learning, predictive analytics

Keywords

• acute kidney injury
• cardiac surgery
• machine learning
• predictive analytics