Approximately two-thirds of all individuals did not exhibit HAND, and with this bias the method favours accuracy in prediction of this group. However, the preference for HIV management is to predict those with HAND with the extra expense related to extensive neurological testing of those without HAND outweighed by availability of treatment IDH inhibitor drugs to those with NP impairment. We therefore weighted prediction of those with HAND to at least 70% accuracy by duplicating the data from 30 randomly chosen individuals with

HAND and adding these to the original data set. The application of SVM to a data set consists of two steps. The first, called the ‘training phase’, consists of using the SVM on a subset of the data to determine optimal values of the parameters w and γ. The second, called the ‘testing phase’, involves applying this choice of parameters to the remainder of the data set to determine the accuracy of the procedure. The accuracy of the training phase is the percentage of data points within the training set that have . The accuracy of the testing phase is similarly defined. The SB431542 order training and testing

phases were conducted using two-thirds of the data randomly chosen for the training set and the remaining one-third for the testing set. As these methods require the selection of tuning parameters such as v in the SVM formulation above, a preliminary training and testing phase was first carried out to determine the tuning parameters

and predictor coefficients w that achieved Thiamet G maximal testing efficacy. The tuning parameters required in the pq−SVM method were calculated over the grid where [27,28]. The steps of randomly choosing two-thirds of the data for training, the calculation of optimal parameters over the grid of values, and the choice of tuning parameters and predictor coefficients that achieve maximal testing efficiency were then repeated 1000 times. The aim of the repeated simulations was to ensure that there were scenarios that achieved a range of predictive capabilities for those without NP impairment, as we wished to limit the number of false positives. The optimal predictor coefficients for each scenario were determined from the best of these 1000 simulations that also achieved at least 70% efficiency (or closest to this constraint) in predicting those with impairment and those without. We applied the SVM with feature selection to the data for the 97 HIV-positive individuals with advanced disease, 36 of whom had been assessed as having HAND, while the remainder were assessed as not having HAND.