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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Oscillometric and spirometric exams were performed in 82 individuals, including controls (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200930) and patients with systemic sclerosis with normal (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200922) and abnormal (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200930) 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).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results and discussion<\/jats:title>\n                    <jats:p>\n                      The first experiment of this study showed that the best oscillometric parameter (BOP) was dynamic compliance, which provided moderate accuracy (AUC\u2009=\u20090.77) 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\u2009=\u20090.94). In the second experiment, the ML techniques were used. In CGvsPSNS, KNN achieved the best result (AUC\u2009=\u20090.90), significantly improving the accuracy in comparison with the BOP (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01), while in CGvsPSAS, RF obtained the best results (AUC\u2009=\u20090.97), also significantly improving the diagnostic accuracy (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05). In the third, fourth, fifth, and sixth experiments, different feature selection techniques allowed us to spot the best oscillometric parameters. They resulted in a small increase in diagnostic accuracy in CGvsPSNS (respectively, 0.87, 0.86, 0.82, and 0.84), while in the CGvsPSAS, the best classifier's performance remained the same (AUC\u2009=\u20090.97).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Oscillometric 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12938-021-00865-9","type":"journal-article","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T17:02:54Z","timestamp":1616691774000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis"],"prefix":"10.1186","volume":"20","author":[{"given":"Domingos S. 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