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Information Journal Paper

Title

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

Pages

  0-0

Abstract

 Introduction: 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. Methods: A dataset of 1000 samples collected in nearly two years was used. Classifi cation methods used for modeling included the support vector machine (SVM), logistic regression, Naï ve 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 fi nal method. Results: 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 specifi city of the fi nal 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. Conclusion: Considering its high sensitivity, the proposed model seems to be suffi ciently reliable for determining the need for performing a CT scan.

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    APA: Copy

    Shahverdi Kondori, Mohsen, & Malek, hamed. (2021). Determining the Need for Computed Tomography Scan Following Blunt Chest Trauma through Machine Learning Approaches. ARCHIVES OF ACADEMIC EMERGENCY MEDICINE (EMERGENCY), 9(1), 0-0. SID. https://sid.ir/paper/772544/en

    Vancouver: Copy

    Shahverdi Kondori Mohsen, Malek hamed. Determining the Need for Computed Tomography Scan Following Blunt Chest Trauma through Machine Learning Approaches. ARCHIVES OF ACADEMIC EMERGENCY MEDICINE (EMERGENCY)[Internet]. 2021;9(1):0-0. Available from: https://sid.ir/paper/772544/en

    IEEE: Copy

    Mohsen Shahverdi Kondori, and hamed Malek, “Determining the Need for Computed Tomography Scan Following Blunt Chest Trauma through Machine Learning Approaches,” ARCHIVES OF ACADEMIC EMERGENCY MEDICINE (EMERGENCY), vol. 9, no. 1, pp. 0–0, 2021, [Online]. Available: https://sid.ir/paper/772544/en

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