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Extreme gradient boosting machine learning method for predicting medical treatment in patients with acute bronchiolitis

Mateo J., Rius-Peris J.M., Maraña-Pérez A.I., Valiente-Armero A., Torres A.M.

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 2

792--801

Acute bronchiolitis is the most common lower respiratory tract infection of infancy. About 2% of infants under 12 months of age hospitalized with this condition each epidemic season. The choice of the correct treatment is important for the evolution of the disease. Therefore, a prediction model for medical treatment identification based on extreme gradient boosting (XGB) machine learning (ML) method is proposed in this paper. Four supervised machine learning algorithms including a k-nearest neighbours (KNN), decision tree (DT), Gaussian Naı¨ve Bayes (GNB) and support vector machine (SVM) were compared with the proposed XGB method. The performance of these methods was then tested implementing a standard 10-fold cross-validation process. The results indicate that the XGB has the best prediction accuracy (94%), high precision (>0.94) and high recall (>0.94). The KNN, SVM, and DT approaches also present moderate prediction accuracy (>87), moderate specificity (>0.87) and moderate sensitivity (>0.87). The GNB algorithm show relatively low classification performance. Based on these results for classification performance and prediction accuracy, the XGB is a solid candidate for a correct classification of patients to be treated. These findings suggest that XGB systems trained with clinical data may serve as a new tool to assist in the treatment of patients with acute bronchiolitis.