Using Machine Learning Technique to Predict the Most Reliable Diagnostic Finding for Foreign Body Aspiration in Children: Symptoms, Chest X-ray, or Auscultation?

Foreign body aspiration (FBA) is one of the most critical and life-threatening pediatric emergency situations. Prompt diagnosis in these cases is very important as they are associated with high mortality among children. When diagnosing FBA, symptoms of the patient, auscultation findings, and chest X-ray findings are usually evaluated. In this study, we conducted a retrospective analysis of all the cases involving suspicion of FBA in children under the age of 18 years who were hospitalized in the Department of Pediatric Surgery at Denizli Pamukkale University Hospital, Turkey from January 2005 to September 2020. Instead of traditional statistical methods, we used machine learning techniques such as random forest and logistic regression to determine which finding was diagnostically the most reliable. The variables included in the analysis that were considered to be significant were as follows: symptoms, auscultation findings, chest X-ray findings, patient gender, age, location of the foreign body, and the time of admission. For the purpose of this study, we developed four different models. Model 1 included gender, age, time of admission, location, and symptoms as variables; the correct classification rate of the model was found to be 82.3%. Model 2 included auscultation findings in addition to Model 1, and the correct classification rate of the model was 84.8%. Model 3 included chest X-ray findings in addition to Model 1, and the correct classification rate of the model was 87.4%. Model 4, on the other hand, included both auscultation findings and chest X-ray findings in addition to Model 1, and the correct classification rate of the model was 87.6%. Based on our findings, a definitive diagnosis of FBA using only symptoms, auscultation findings, or chest X-ray findings in isolation does not seem possible. Additionally, using only symptoms and chest X-ray findings is also insufficient to make a diagnosis.