Prediction of traumatic pathology by classifying thorax trauma using a hybrid method for emergency services

Abstract

In recent years, data mining and algorithm-based methods have been used frequently for the prediction and diagnosis of various diseases. Traumas, being one of the significant health problems in the world, are also one of the most important causes of death. This study aims to predict the presence of traumatic pathology in the lung of the patients admitted to the emergency department due to blunt thorax trauma with no X-ray and computed tomography (CT) history by machine learning methods. The models developed in the study using the 5-fold cross-validation method are most accurately classified by the ensemble (voting) classifier, whether there is a pathology in X-ray (mean accuracy = 0.82) and CT (mean accuracy = 0.83). The K-nearest neighbourhood method classifies patients with pathology in X-ray by 83% accuracy, while the ensemble (voting) method classifies non-pathology patients by 94% accuracy in models. Of CT results, random forest, ensemble (voting), and ensemble (stacking) classifiers are precisely classified by 96%, while those patients with pathology are classified perspicuously by 77%. As a result, a mathematical framework using data mining methods was proposed based on estimating the X-ray and CT results for the thorax graph scan.