Paper Title
Support Vector Machınes wıth Indıvıdual Combınatıons of Relıef-F and mRMR For Predıctıng VO2max From Maxımal and Questıonnaıre Data

Maximum oxygen uptake (VO2max) is widely accepted as being a reliable and valid measure of cardiorespiratory fitness (CRF). In this paper, Support Vector Machine (SVM) with individual combinations of Relief-F and minimum redundancy maximum relevance (mRMR) feature selection algorithms has been used to build new VO2max prediction models from maximal and questionnaire data, with the aim to compare the performance of Relief-F with mRMR and to identify the discriminative predictors of VO2max. The dataset is made up of 440 (230 females, 210 males) volunteers ranging in age from 20 to 79, and includes the physiological variables gender, age, body mass (BM) and height; the maximal variables heart rate (HRmax), rating of perceived exertion (RPE), respiratory exchange ratio (RER) and exercise time; and finally the questionnaire variable activity code (AC). The dataset has been randomly divided into training and test sets by applying 10-fold cross validation. The correlation coefficients (R’s) and root mean square errors (RMSE's) have been calculated to assess the performance of the prediction models. The results reveal that the prediction model containing all physiological, maximal and questionnaire variables yields the highest R and lowest RMSE with 0.95 and 3.79 mL kg-1 min-1, respectively. As for the importance of the variables for VO2max prediction; it turns out that exercise time is the most important predictor variable, independent of whether Relief-F or mRMR has been applied on the dataset. Furthermore, it is seen that mRMR in average exhibits slightly better performance than Relief-F for prediction of VO2max. To draw a comparison with the performance of SVM, prediction models based on decision tree forest (DTF) and radial basis function neural network (RBFNN) have also been developed, and it turns out that SVM comparatively yields higher R’s and lower RMSE’s than other methods for prediction of VO2max. Keywords- SVM, RBFNN, DTF, Relief-F, mRMR, Maximal Oxygen Uptake, Prediction.