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Advisor(s)
Abstract(s)
The Cardiopulmonary Exercise Test (CPET) is a diagnostic test that evaluates the functional capacity of an individual through the integrated response of the cardiovascular, respiratory, and metabolic systems. VO2 max is the parameter that access functional capacity, although it’s difficult to achieve given the effort that implies. In recent years, an increase in computing capabilities combined with the available storage of large amounts of information has led to a heightened interest in machine learning (ML). We aimed in this study to enable CPET with ML models that allow predicting oxygen consumption in healthy individuals. The study methodology is based on the cleaning and exploratory analysis of a public database with about 992 CPETs performed on healthy individuals and athletes. To predict each value of VO2 (~569,000 instances), five ML algorithms were used (Random Forests, kNN, Neural Networks, Linear Regression, and SVM) with heart rate, respiratory rate, time from the beginning of the exam and treadmill speed, using a 20-fold cross-validation. The best result came from the Random Forest model, with a R2 of 0.88 and a RMSE of 334.34 ml.min-1. Furthermore, using the same methodology but different features, we tried to predict the VO2max with the 724 adult participants with a maximal test (RER≥1.05) but weaker results were obtained (the best model was the Linear Regression, with a R2 of 0.50 and a RMSE of 498.06 ml.min-1). Still, this model showed a better correlation with the real VO2max than the Wasserman equation (R=0.71 vs R=0.59). It is possible to predict with accuracy breath-by-breath VO2, based on easy-to-obtain physiological and mechanical measurements.
Description
Keywords
Cardiopulmonary Exercise Test Machine learning Physical activity Physiological diagnostic services
Citation
Pereira MP, Almeida R, Dias H. VO2 prediction based on physiologic and mechanical exercise measurements. In: ERS International Congress 2022, Barcelona (Spain), September 4-6, 2022. Eur Respir J. 2022;60(Suppl):4270.
Publisher
European Respiratory Society