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

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This study introduces a machine learning model to reduce unnecessary computed tomography (CT) scans for chest trauma patients. The decision tree model achieved high accuracy, ensuring reliable decisions for CT scan necessity.

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Area of Science:

  • Emergency Medicine
  • Machine Learning
  • Medical Imaging

Background:

  • Computed tomography (CT) scans are crucial for diagnosing trauma patients.
  • Machine learning advances enable more accurate and rapid medical diagnoses.
  • Reducing unnecessary CT scans in emergency medicine is a key objective.

Purpose of the Study:

  • To develop a machine learning-based method to assist emergency physicians.
  • To prevent the performance of unnecessary CT scans for chest trauma patients.

Main Methods:

  • A dataset of 1000 samples was analyzed.
  • Seven classification methods were evaluated: SVM, logistic regression, Naïve Bayes, decision tree, multilayer perceptron, random forest, and KNN.
  • A decision tree approach was selected as the final model due to its interpretability.

Main Results:

  • The decision tree algorithm demonstrated superior accuracy compared to other methods.
  • The optimal tree depth was determined to be 7.
  • The final model achieved 99.91% accuracy, 100% sensitivity, and 99.33% specificity.

Conclusions:

  • The proposed machine learning model is highly reliable for determining the necessity of CT scans.
  • High sensitivity of the model supports its clinical utility in reducing unnecessary procedures.