Predicting cardiorespiratory parameters based on endurance testingCUNDRIČ, LARSEN (Avtor) Bosnić, Zoran (Mentor) Popović, Dejana (Komentor) machine learningmaximal heart ratefeature selectionmaximal oxygen uptakecardiorespiratory fitnessMaximal heart rate (HRmax) and Maximal oxygen consumption (VO2max) are measures of adequate effort during an exercise test. Current equations for HRmax and VO2max prediction are not sufficiently accurate. Our aim was to improve those predictions using machine learning (ML). We used a sample of the Fitness Registry and the Importance of Exercise: An International Data Base (FRIEND Registry) with 17,325 healthy individuals (81% males) who performed a maximal cardiopulmonary exercise test (CPX). Mean age was 45.81±12.54 years, HRmax was 162.49±20.07 bpm and VO2max 32.51±11.13 mlO2 kg^-1 min^-1. Different ML algorithms were used to predict HRmax and VO2max: lasso regression, random forests, neural networks, and support vector machine. Prediction accuracy was measured using the root mean squared error (RMSE), relative RMSE (RRMSE), Pearson correlation test and Bland-Altman analysis. The best predictive model was explained using the Shapley’s Additive Explanations (SHAP). For ML predictions we used age, resting heart rate, weight, height and resting systolic and diastolic blood pressure based on RReliefF feature selection. All models improved HRmax and VO2max prediction and decreased RMSE and RRMSE compared to baseline formulas.20212021-10-13 16:10:00Diplomsko delo/naloga132134sl