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

Content Provider	IET Digital Library
Author	Karaci, Abdulkadir Ozkaraca, Osman Acar, Ethem Demir, Ahmet
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.
Starting Page	754
Ending Page	764
Page Count	11
ISSN	17519675
Volume Number	14
e-ISSN	17519683
Issue Number	Issue 10, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/14/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2020.0014
Journal	IET Signal Processing
Publisher Date	2021-01-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algorithm-based Method Biology And Medical Computing Biomedical Imaging/measurement Combinatorial Mathematics Computed Tomography History Computer Vision And Image Processing Technique Computerised Tomography Cross-validation Method CT Result Data Mining Data Mining Method Diseases Emergency Services Ensemble Classifier Health Problem Hybrid Method Image Classification Image Recognition K-nearest Neighbourhood Method Learning in AI Lung Machine Learning Method Medical Image Processing Nearest Neighbour Method Nonpathology Patients Patient Diagnostic Method And Instrumentation Probability Theory Radiography And Computed Tomography Random Forest Statistics Stochastic Linearised SCUC Thorax Graph Scan Thorax Trauma Traumatic Pathology X-Ray Technique X-Rays And Particle Beam
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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