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Explainable machine learning methods and respiratory oscillometry for the diagnosis of respiratory abnormalities in sarcoidosis

de Lima, Allan Danilo ; Lopes, Agnaldo J ; do Amaral, Jorge Luis Machado ; de Melo, Pedro Lopes

BMC medical informatics and decision making, 2022-10, Vol.22 (1), p.1-274, Article 274 [Periódico revisado por pares]

London: BioMed Central Ltd

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Título:
Explainable machine learning methods and respiratory oscillometry for the diagnosis of respiratory abnormalities in sarcoidosis
Autor: de Lima, Allan Danilo ; Lopes, Agnaldo J ; do Amaral, Jorge Luis Machado ; de Melo, Pedro Lopes
Assuntos: Abnormalities ; Airway management ; Algorithms ; Analysis ; Artificial intelligence ; Care and treatment ; Chronic obstructive pulmonary disease ; Classification ; Classifiers ; Clinical decision support system ; Decision support systems ; Decision trees ; Diagnosis ; Evaluation ; Forced oscillation technique ; Forced vibration ; Genetic algorithms ; Health informatics ; Laboratories ; Learning algorithms ; Machine learning ; Medical diagnosis ; Methods ; Oscillation ; Patient compliance ; Programming ; Pulmonary functions ; Respiratory diseases ; Respiratory function ; Respiratory system ; Sarcoidosis ; Spirometry ; Support vector machines ; Workflow
É parte de: BMC medical informatics and decision making, 2022-10, Vol.22 (1), p.1-274, Article 274
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SourceType-Scholarly Journals-1
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Descrição: Abstract Background In this work, we developed many machine learning classifiers to assist in diagnosing respiratory changes associated with sarcoidosis, based on results from the Forced Oscillation Technique (FOT), a non-invasive method used to assess pulmonary mechanics. In addition to accurate results, there is a particular interest in their interpretability and explainability, so we used Genetic Programming since the classification is made with intelligible expressions and we also evaluate the feature importance in different experiments to find the more discriminative features. Methodology/principal findings We used genetic programming in its traditional tree form and a grammar-based form. To check if interpretable results are competitive, we compared their performance to K-Nearest Neighbors, Support Vector Machine, AdaBoost, Random Forest, LightGBM, XGBoost, Decision Trees and Logistic Regressor. We also performed experiments with fuzzy features and tested a feature selection technique to bring even more interpretability. The data used to feed the classifiers come from the FOT exams in 72 individuals, of which 25 were healthy, and 47 were diagnosed with sarcoidosis. Among the latter, 24 showed normal conditions by spirometry, and 23 showed respiratory changes. The results achieved high accuracy (AUC > 0.90) in two analyses performed (controls vs. individuals with sarcoidosis and normal spirometry and controls vs. individuals with sarcoidosis and altered spirometry). Genetic Programming and Grammatical Evolution were particularly beneficial because they provide intelligible expressions to make the classification. The observation of which features were selected most frequently also brought explainability to the study of sarcoidosis. Conclusions The proposed system may provide decision support for clinicians when they are struggling to give a confirmed clinical diagnosis. Clinicians may reference the prediction results and make better decisions, improving the productivity of pulmonary function services by AI-assisted workflow.
Editor: London: BioMed Central Ltd
Idioma: Inglês

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Explainable machine learning methods and respiratory oscillometry for the diagnosis of respiratory abnormalities in sarcoidosis

de Lima, Allan Danilo ; Lopes, Agnaldo J ; do Amaral, Jorge Luis Machado ; de Melo, Pedro Lopes

London: BioMed Central Ltd

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