Enhancing Post-Surgical Complication Prediction Using Random Forest and Neural Network Algorithms in Machine Learning

Abstract

This research paper explores the application of machine learning algorithms, specifically Random Forest and Neural Networks, to enhance the prediction of post-surgical complications, which is critical for improving patient outcomes and optimizing healthcare resources. The study involves a comprehensive analysis of a large dataset comprising preoperative, intraoperative, and postoperative variables collected from several healthcare facilities. Initially, extensive data preprocessing techniques, including normalization, imputation of missing values, and feature selection, were employed to prepare the dataset for model training. The Random Forest algorithm was utilized for its robustness in handling complex interactions between variables and its ability to provide feature importance metrics, aiding in the identification of key predictors of post-surgical complications. Concurrently, a Neural Network model was developed to capture non-linear relationships within the data, leveraging its capacity to model intricate patterns through multiple hidden layers. The performance of both models was evaluated using metrics such as accuracy, precision, recall, and F1-score, with a particular focus on the area under the receiver operating characteristic curve (AUC-ROC) to assess the discriminative ability of each algorithm. Results indicated that while both models demonstrated significant improvements over traditional statistical methods, the Neural Network exhibited superior performance in capturing complex interactions, as evidenced by a higher AUC-ROC score. The study concludes with a discussion on the implications of these findings for clinical decision-making, emphasizing the potential of integrating machine learning models into existing healthcare systems to facilitate early identification and intervention for patients at risk of post-surgical complications. Future work will focus on refining these models to enhance generalizability and exploring the integration of real-time data for dynamic prediction updates.