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Machine Learning Associated With Respiratory Oscillometry: A Computer-Aided Diagnosis System for the Detection of Respiratory Abnormalities in Systemic Sclerosis
- Source :
- BioMedical Engineering, BioMedical Engineering OnLine, Vol 20, Iss 1, Pp 1-18 (2021)
- Publication Year :
- 2021
- Publisher :
- Research Square Platform LLC, 2021.
-
Abstract
- IntroductionThe use of machine learning (ML) methods would improve the diagnosis of respiratory changes in systemic sclerosis (SSc). This paper evaluates the performance of several ML algorithms associated with the respiratory oscillometry analysis to aid in the diagnostic of respiratory changes in SSc. We also find out the best configuration for this task.MethodsOscillometric and spirometric exams were performed in 82 individuals, including controls (n = 30) and patients with systemic sclerosis with normal (n = 22) and abnormal (n = 30) spirometry. Multiple instance classifiers and different supervised machine learning techniques were investigated, including k-Nearest Neighbors (KNN), Random Forests (RF), AdaBoost with decision trees (ADAB), and Extreme Gradient Boosting (XGB).Results and discussionThe first experiment of this study showed that the best oscillometric parameter (BOP) was dynamic compliance, which provided moderate accuracy (AUC = 0.77) in the scenario control group versus patients with sclerosis and normal spirometry (CGvsPSNS). In the scenario control group versus patients with sclerosis and altered spirometry (CGvsPSAS), the BOP obtained high accuracy (AUC = 0.94). In the second experiment, the ML techniques were used. In CGvsPSNS, KNN achieved the best result (AUC = 0.90), significantly improving the accuracy in comparison with the BOP (p p ConclusionsOscillometric principles combined with machine learning algorithms provide a new method for diagnosing respiratory changes in patients with systemic sclerosis. The present study's findings provide evidence that this combination may help in the early diagnosis of respiratory changes in these patients.
- Subjects :
- Male
Clinical decision support system
computer.software_genre
030218 nuclear medicine & medical imaging
Machine Learning
0302 clinical medicine
Diagnosis, Computer-Assisted
AdaBoost
Respiratory system
Radiological and Ultrasound Technology
medicine.diagnostic_test
General Medicine
Middle Aged
Random forest
Forced oscillation technique
lcsh:R855-855.5
Systemic sclerosis
Female
Algorithms
Adult
Spirometry
lcsh:Medical technology
Biometry
Adolescent
Biomedical Engineering
Decision tree
Feature selection
Diagnostic of respiratory diseases
Machine learning
Biomaterials
Young Adult
03 medical and health sciences
Artificial Intelligence
Oscillometry
medicine
Humans
Radiology, Nuclear Medicine and imaging
System identification techniques
Aged
Scleroderma, Systemic
Computers
business.industry
Research
Respiration Disorders
Respiratory oscillometry
030228 respiratory system
Computer-aided diagnosis
Artificial intelligence
business
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- BioMedical Engineering, BioMedical Engineering OnLine, Vol 20, Iss 1, Pp 1-18 (2021)
- Accession number :
- edsair.doi.dedup.....2712328ed52bab3f0d70ab0a11389eb7