Prognostic machine learning models for predicting postoperative complications following general surgery in Bandar Abbas, Iran: a study protocol

IntroductionTo enhance the quality of surgical care, complications need to be minimised. Consequently, comprehending the occurrence and risk elements for postoperative complications is essential. Subsequently, we will apply machine learning (ML) algorithms to build risk factor prediction models that...

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Bibliographic Details
Published in:BMJ open Vol. 15; no. 10; p. e108019
Main Authors: Vatankhah Tarbebar, Majid, Mohammadi, Milad, Mehrnoush, Vahid, Darsareh, Fatemeh
Format: Journal Article
Language:English
Published: England British Medical Journal Publishing Group 28.10.2025
BMJ Publishing Group LTD
BMJ Publishing Group
Subjects:
Adult
Algorithms
Antibiotics
Anticoagulants
Artificial intelligence
Blood transfusions
Cardiac arrest
COVID-19 vaccines
Drug use
Female
Humans
Hypertension
Immunization
Iran
Kidney diseases
Machine Learning
Male
Marital status
Missing data
Molecular weight
Mortality
Patients
Pneumonia
Postoperative Complications - diagnosis
Postoperative Complications - epidemiology
Postoperative Complications - etiology
Prognosis
Quality improvement
Questionnaires
Regression analysis
Research Design
Risk Assessment - methods
Risk Factors
Sepsis
Surgery
Variables
Ventilators
Iran
SURGERY
Machine Learning
Risk Factors
ISSN:2044-6055, 2044-6055
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Description
Summary:IntroductionTo enhance the quality of surgical care, complications need to be minimised. Consequently, comprehending the occurrence and risk elements for postoperative complications is essential. Subsequently, we will apply machine learning (ML) algorithms to build risk factor prediction models that will assist surgeons in identifying the risk factors associated with the development of postoperative complications after general surgery.Methods and analysisThis research will take place at a tertiary referral medical centre located in Bandar Abbas, Hormozgan, Iran. The inclusion criteria are (1) individuals aged 18 years or older who have any type of general surgery and (2) hospitalised from September 2025 to September 2026. Individuals with insufficient data will be excluded. The main outcomes of the study are complications within 30 days of surgery. Patients will be divided into two groups based on whether they develop complications or not. The predictors are classified as (1) patient-related factors, (2) surgery-related factors and (3) postoperative factors. We intend to detect postoperative complications following general surgery using four distinct supervised ML techniques: (1) logistic regression, (2) decision trees, (3) random forests and (4) extreme gradient boosting. Accuracy, precision, recall and F1 score will be used to evaluate the performance of ML models.Ethics and disseminationWith approval from the Hormozgan University of Medical School Research Ethics Board (IR.HUMS.REC.1404.137), we will carry out a forward-looking analysis of the medical records of patients undergoing general surgery. We will obtain informed consent, and all information will be collected and examined anonymously. The findings of this research will be released in appropriate scientific publications.
Bibliography:Protocol
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ISSN:2044-6055
2044-6055
DOI:10.1136/bmjopen-2025-108019