Annals of Operations Research https://doi.org/10.1007/s10479-020-03872-6 S.I.: ARTIFICIAL INTELLIGENCE IN OPERATIONS MANAGEMENT Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis Marina Johnson 1 · Abdullah Albizri 1 · Serhat Simsek 1 Accepted: 8 November 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Artificial Intelligence (AI) is critical for data-driven decision making to increase resource utilization, operational performance, and service quality in various industry domains, partic- ularly in healthcare. Using AI in healthcare operations can significantly improve treatment outcomes and enhance patient satisfaction while reducing costs. In this paper, we propose a multi-stage framework to build an AI-based decision support tool that can predict the 5- year survivability of lung cancer patients. We evaluate the proposed framework using the Surveillance, Epidemiology, and End Results dataset pertaining to the 1973–2015 period obtained from the National Institutes of Health. The first stage entails data preprocessing and target creation. The second stage applies six AI algorithms with feature selection through Particle Swarm Optimization and hyperparameter tuning with Cross-Validation. These Algo- rithms include Logistic Regression, Decision Trees, Random Forests (RF), Adaptive Boosting (AdaBoost), Artificial Neural Network, and Naïve Bayes. The results show that RF and AdaBoost models yield an AUC rate of 0.94 and outperform the other models. Stage 3 utilizes permutation importance to interpret the RF and AdaBoost models and applies Tree- based Augmented Naïve Bayes to gain insights regarding the interrelations among important features. The results of Stage 3 delineate that the number of lymph nodes containing metas- tases), the number of tumors that patients have had in their lifetime, the patient’s age, and the microscopic composition of cells rank among the topmost important features and can signif- icantly impact patient survivability. We think this study has significant practical implications in helping physicians predict prognosis and develop treatment plans for lung cancer patients. Keywords Artificial intelligence · Machine learning · Healthcare operations · Cancer survival prediction · Healthcare analytics B Abdullah Albizri albizria@montclair.edu 1 Feliciano School of Business, Montclair State University, Montclair, NJ, USA 123