Comput Methods Programs Biomed - An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms.

Tópicos

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Resumo

The purpose of this study was to develop an automatic classifier to increase the accuracy of the forced oscillation technique (FOT) for diagnosing early respiratory abnormalities in smoking patients. The data consisted of FOT parameters obtained from 56 volunteers, 28 healthy and 28 smokers with low tobacco consumption. Many supervised learning techniques were investigated, including logistic linear classifiers, k nearest neighbor (KNN), neural networks and support vector machines (SVM). To evaluate performance, the ROC curve of the most accurate parameter was established as baseline. To determine the best input features and classifier parameters, we used genetic algorithms and a 10-fold cross-validation using the average area under the ROC curve (AUC). In the first experiment, the original FOT parameters were used as input. We observed a significant improvement in accuracy (KNN=0.89 and SVM=0.87) compared with the baseline (0.77). The second experiment performed a feature selection on the original FOT parameters. This selection did not cause any significant improvement in accuracy, but it was useful in identifying more adequate FOT parameters. In the third experiment, we performed a feature selection on the cross products of the FOT parameters. This selection resulted in a further increase in AUC (KNN=SVM=0.91), which allows for high diagnostic accuracy. In conclusion, machine learning classifiers can help identify early smoking-induced respiratory alterations. The use of FOT cross products and the search for the best features and classifier parameters can markedly improve the performance of machine learning classifiers.

Resumo Limpo

purpos studi develop automat classifi increas accuraci forc oscil techniqu fot diagnos earli respiratori abnorm smoke patient data consist fot paramet obtain volunt healthi smoker low tobacco consumpt mani supervis learn techniqu investig includ logist linear classifi k nearest neighbor knn neural network support vector machin svm evalu perform roc curv accur paramet establish baselin determin best input featur classifi paramet use genet algorithm fold crossvalid use averag area roc curv auc first experi origin fot paramet use input observ signific improv accuraci knn svm compar baselin second experi perform featur select origin fot paramet select caus signific improv accuraci use identifi adequ fot paramet third experi perform featur select cross product fot paramet select result increas auc knnsvm allow high diagnost accuraci conclus machin learn classifi can help identifi earli smokinginduc respiratori alter use fot cross product search best featur classifi paramet can mark improv perform machin learn classifi

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