Prediction of Early Mortality in Patients Undergoing Right Hemicolectomy Using Machine Learning Algorithms

Abstract

Aim:This study aims to determine whether early mortality in patients undergoing right hemicolectomy can be predicted using artificial intelligence (machine learning) algorithms. Method:The study included all cases of right hemicolectomy or extended right hemicolectomy performed in our clinic between January 2019 and December 2023. Data were collected retrospectively from a prospectively maintained database. Patients undergoing surgeries other than right hemicolectomy were excluded. A database was created using basic clinical data and processed in the Google Colab environment using TensorFlow, Keras, Pandas, Numpy, and Scikit-learn libraries. The TensorFlow. Keras Sequential model was used with “Dense” layers, and the “Adam” optimizer was chosen for optimization. Eighty percent of the database was used for training, and 20% was used for testing. Results:The database included 410 patients, of whom 258 (62.9%) were male, and 152 (37.1%) were female, with a mean age of 63.5±14.2 years. Early mortality occurred in 39 (9.5%) patients. Data from 307 patients were used for machine learning training, and data from 103 patients were used for testing. The machine-learning model predicted early mortality with an accuracy range of 91% to 95% using basic clinical parameters.. Conclusion:This study demonstrates that early mortality in patients undergoing right hemicolectomy can be predicted with high accuracy using machine learning algorithms. The above 90% accuracy rate achieved using basic clinical parameters indicates the potential of this algorithm as a supportive tool in clinical decision-making processes.