Comput Methods Programs Biomed - Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease.

Tópicos

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Resumo

The purpose of this study is to develop a clinical decision support system based on machine learning (ML) algorithms to help the diagnostic of chronic obstructive pulmonary disease (COPD) using forced oscillation (FO) measurements. To this end, the performances of classification algorithms based on Linear Bayes Normal Classifier, K nearest neighbor (KNN), decision trees, artificial neural networks (ANN) and support vector machines (SVM) were compared in order to the search for the best classifier. Four feature selection methods were also used in order to identify a reduced set of the most relevant parameters. The available dataset consists of 7 possible input features (FO parameters) of 150 measurements made in 50 volunteers (COPD, n = 25; healthy, n = 25). The performance of the classifiers and reduced data sets were evaluated by the determination of sensitivity (Se), specificity (Sp) and area under the ROC curve (AUC). Among the studied classifiers, KNN, SVM and ANN classifiers were the most adequate, reaching values that allow a very accurate clinical diagnosis (Se > 87%, Sp > 94%, and AUC > 0.95). The use of the analysis of correlation as a ranking index of the FOT parameters, allowed us to simplify the analysis of the FOT parameters, while still maintaining a high degree of accuracy. In conclusion, the results of this study indicate that the proposed classifiers may contribute to easy the diagnostic of COPD by using forced oscillation measurements.

Resumo Limpo

purpos studi develop clinic decis support system base machin learn ml algorithm help diagnost chronic obstruct pulmonari diseas copd use forc oscil fo measur end perform classif algorithm base linear bay normal classifi k nearest neighbor knn decis tree artifici neural network ann support vector machin svm compar order search best classifi four featur select method also use order identifi reduc set relev paramet avail dataset consist possibl input featur fo paramet measur made volunt copd n healthi n perform classifi reduc data set evalu determin sensit se specif sp area roc curv auc among studi classifi knn svm ann classifi adequ reach valu allow accur clinic diagnosi se sp auc use analysi correl rank index fot paramet allow us simplifi analysi fot paramet still maintain high degre accuraci conclus result studi indic propos classifi may contribut easi diagnost copd use forc oscil measur

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