Towards an Intelligent Surgical Preoperative Decision Support System: An Approach Based on Decision Trees and the Random Forest


Surgery, Preoperative phase, Artificial Intelligence, Machine Learning, Decision Tree, Random Forest


In recent years, Artificial Intelligence has experienced significant growth thanks to technological advances such as high performance computing and massive data processing. This evolution has led to a new reflection as well as a serious interest on the part of managers of hospital structures, who are beginning to take an interest in the automation and optimization of surgical processes. Machine Learning finds its place as a preferred technique for developing intelligent decision support systems in the operating room. In this aisle, it has become crucial to automate and optimize preoperative procedures. Through this paper, we focus on the development of an intelligent decision support system for the surgical preoperative phase, using the Random Forest model, which is an extension of the decision tree algorithm, to analyze a variety of preoperative predictive data such as hypertension, body temperature, ECG, hemoglobin, etc. We use a dataset previously approved by an institutional review board, while validating our model in a development environment dedicated to the field of Artificial Intelligence.


Download data is not yet available.


  • A,Bataineh, A Comparative Analysis of Nonlinear Machine Learning Algorithms for Breast Cancer Detection. International Journal of Machine Learning and Computing, A. (2019). Vol 9, 248-254.
  • A.Géron, "Hands-On Machine Learning with Scikit-Learn and TensorFlow", concepts, tools, and techniques to build intelligent systems, 1st Edition. Beijing ; Boston: O’Reilly Media, 2017.
  • A.Géron, "Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow": concepts, tools, and techniques to build intelligent systems, Second edition. Beijing China ; Sebastopol, CA: O’Reilly Media, Inc, 2019.
  • B.Bilal. Distributed Artificial Intelligence approach for reactive planning and assistance in the conduct of the hospital operating theater process. Doctoral thesis. Presented and defended at “Belfort”, on “December 19, 2019”
  • B.Mahesh, "Machine Learning Algorithms - A Review," Int. J.Sci. Res., vol. 9, no. 1, p. 381-386, Jan. 2020. [Online]. Available: DOI: 10.21275/ART20203995
  • D.Tighe, Lewis-Morris T, Freitas A. Machine Learning methods applied to audit of surgical outcomes after treatment for cancer of the head and neck. Br J Oral Maxillofac Surg. 2019;57(8):7717.
  • D.Wang, Li J, Sun Y, Ding X, Zhang X, Liu S, Han B, Wang H, Duan X and Sun T (2021) A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients. Front. Public Health 9:754348. doi: 10.3389/fpubh.2021.754348.
  • D.Zambouri, "Preoperative evaluation and preparation for anesthesia and surgery," Hippocratie, vol. 11, no. 1, p. 13-21, Jan.-Mar. 2007. PMCID: PMC2464262, PMID: 19582171.
  • G.Robin, Jean-Michel Poggi "CART Trees and Random Forests, Importance and Selection of Variables", January 2017.
  • L.Breiman, "Random Forests," Statistics Department, University of California, Berkeley, CA 94720, January 2001.
  • L.Dorard, "Architecture of a Real-World Machine Learning System in Words," Medium, 2020. [Online]. Available:
  • Lee, HC, Park, Y, Yoon, S.B. et al. VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients. Sci Data 9, 279 (2022).
  • Liu, Y.H., Jin, J. & Liu, Y.J. Machine Learning–based Random Forest for predicting decreased quality of life in thyroid cancer patients after thyroidectomy. Support Care Cancer 30, 2507–2513 (2022).
  • M. Durand, A. Shaikh, M. Billi, and E. Lechevallier, "Artificial intelligence applications in medicine: Growing opportunities and research challenges," Progrès en Urologie - FMC, vol. 30, no. 2, p. F63-F68, June 2020. Elsevier. DOI: 10.1016/j.fpurol.2020.02.001.
  • P.Dinesh et K. P., « Medical Image Prediction for Diagnosis of Breast Cancer Disease Comparing the Machine Learning Algorithms: SVM, KNN, Logistic Regression, Random Forest, and Decision Tree to Measure Accuracy », ECS Trans., vol. 107, no 1, p. 12681 12691, avr. 2022, doi: 10.1149/10701.12681ecst.
  • S. Raschka and V. Mirjalili, "Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2," 3rd ed., Expert Insight, Birmingham Mumbai: Packt, 2019, ISBN: 978-1-78995-575-0.
  • Tangirala, Suryakanthi. Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm. International Journal of Advanced Computer Science and Applications, 2020, vol. 11, no 2, p. 612-619.
  • W.Hong, Lu Y, Zhou X, Jin S, Pan J, Lin Q, Yang S, Basharat Z, Zippi M and Goyal H (2022) Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis. Front. Cell. Infect. Microbiol. 12:893294. doi: 10.3389/fcimb.2022.893294.
  • W.Ji, C. Sang, X. Zhang, K. Zhu, and L. Bo, “Personality, Preoperative Anxiety, and Postoperative Outcomes: A Review,” IJERPH, vol. 19, no. 19, p. 12162, Sep 2022, doi: 10.3390/ijerph191912162
  • Z.Md. Alam, M. S. Rahman, et M. S. Rahman, « A Random Forest based predictor for medical data classification using feature ranking », Informatics in Medicine Unlocked, vol. 15, p. 100180, 2019, doi: 10.1016/j.imu.2019.100180.
  • Z.Yalong, Z,Zunni, Liuxiang Wei and Shujing Wei. Construction and validation of nomograms combined with novel Machine Learning algorithms to predict early death of patients with metastatic colorectal cancer. Front. Public Health, 20 December 2022. Sec. Aging and Public Health Volume 10 – 2022.
  • Published: 2023-09-30

    Issue: Vol. 2 No. 3 (2023) (view)

    Section: Review Articles

    How to cite:
    C. BOUDEN and C. MEZIOUD, “Towards an Intelligent Surgical Preoperative Decision Support System: An Approach Based on Decision Trees and the Random Forest”, Intell Methods Eng Sci, vol. 2, no. 3, pp. 67–74, Sep. 2023.

    All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

    IMIENS open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.