Comparative Analysis of Random Forest and CNN for Surgery Mortality Prediction

Accurate surgical outcome forecasting is essential for patient safety, healthcare resource allocation, and healthcare professional's capacity to make well-informed choices. To address this, a paper has been initiated to create a machine learning (ML) and deep learning (DL) model that can effectively determine the likelihood of a patient surviving a surgery. The main goal of this paper is to compare the performance of both models and determine which one yields superior overall results and develop something that can help the healthcare industry in making judgements. To conduct this research, a comprehensive dataset comprising approximately 50,000 surgical cases from various hospitals has been acquired. The paper successfully developed and evaluated various models. The best-performing model was “Random Forest”, which achieved an accuracy of 92.51%. The deep learning models also showed useful results, with the best model achieving an accuracy of 91.3%. The process of fine-tuning the DL model has yielded a significant improvement in accuracy, increasing it from 91.3% to 93.4%.