Determining the Need for Computed Tomography Scan Following Blunt Chest Trauma through Machine Learning Approaches

The use of computed tomography (CT) scan is essential for making diagnoses for trauma patients in emergency medicine. Numerous studies have been conducted on guiding medical examinations in light of advances in machine learning, leading to more accurate and rapid diagnoses. The present study aims to propose a machine learning-based method to help emergency physicians prevent performance of unnecessary CT scans for chest trauma patients.

A dataset of 1000 samples collected in nearly two years was used. Classification methods used for modeling included the support vector machine (SVM), logistic regression, Naïve Bayes, decision tree, multilayer perceptron (four hidden layers), random forest, and K nearest neighbor (KNN). The present work employs the decision tree approach (the most interpretable machine learning approach) as the final method.

The accuracy of 7 machine learning algorithms was investigated. The decision tree algorithm was of higher accuracy than other algorithms. The optimal tree depth of 7 was chosen using the training data. The accuracy, sensitivity and specificity of the final model was calculated to be 99.91% (95%CI: 99.10% - 100%), 100% (95%CI: 99.89% - 100%), and 99.33% (95%CI: 99.10% - 99.56%), respectively.

Considering its high sensitivity, the proposed model seems to be sufficiently reliable for determining the need for performing a CT scan.