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Using Machine Learning Technique to Predict the Most Reliable Diagnostic Finding for Foreign Body Aspiration in
Incinur Genisol1, Osman Uzunlu2
1Pediatric Surgery, Sisli Hamidiye Etfal Training And Research Hospital, Istanbul, TUR.
Insights
Foreign body aspiration (FBA) in children requires prompt diagnosis. Machine learning models show that combining symptoms with chest X-ray and auscultation findings improves diagnostic accuracy for FBA.
Area of Science:
- Pediatric Emergency Medicine
- Medical Diagnostics
- Machine Learning in Healthcare
Background:
- Foreign body aspiration (FBA) is a critical pediatric emergency with high mortality.
- Accurate and timely diagnosis of FBA is crucial for effective treatment.
- Traditional diagnostic methods for FBA include patient symptoms, auscultation, and chest X-rays.
Purpose of the Study:
- To retrospectively analyze pediatric FBA cases to identify the most reliable diagnostic indicators.
- To compare the diagnostic accuracy of machine learning models incorporating various clinical and radiological findings.
- To evaluate the effectiveness of machine learning in diagnosing foreign body aspiration in children.
Main Methods:
- Retrospective analysis of pediatric FBA cases (under 18 years) hospitalized between January 2005 and September 2020.
- Application of machine learning techniques, including random forest and logistic regression.
- Development of four models incorporating variables such as symptoms, auscultation, chest X-ray, age, gender, location, and time of admission.
Main Results:
- Model 1 (symptoms, demographics, location, time) achieved 82.3% accuracy.
- Model 2 (including auscultation) reached 84.8% accuracy.
- Model 3 (including chest X-ray) achieved 87.4% accuracy, and Model 4 (including both auscultation and chest X-ray) reached 87.6% accuracy.
Conclusions:
- Diagnosing FBA solely based on symptoms, auscultation, or chest X-ray findings in isolation is insufficient.
- Combining clinical symptoms with chest X-ray and auscultation findings significantly enhances diagnostic accuracy for pediatric FBA.
- Machine learning models demonstrate a powerful capability in improving the diagnostic reliability of foreign body aspiration in children.
Abstract:
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.
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