## 4 Data Analysis and Discriminatory Models Used in Breath Biomarker Research
### Data Analysis
SVM method is primarily a binary classification method and in simple terms it converts the data via specific functions called kernels, to obtain a big map of nonlinear variables [252]. Hence, nonlinear techniques, particularly Kernel methods, are more powerful in terms of prediction accuracy and discrimination and are therefore naturally suitable for classification of volatomics data into, for instance, cancer and non-cancer categories [266,267]. More recently, ANNs are becoming popular in medical diagnosis [264]. ANNs is a fast, robust and highly predictive approach, involving powerful and flexible methods (it is a Euclidean metric based system that can handle complex data space) to model nonlinear problems. Although the interpretation of the effects of a compound is complicated, variants of ANNs have been applied in the analysis of sensor array results, MS data, and IMS measurements, among others [252]. An independent study using chemical sensors, for instance, has shown that ANNs is a preferred algorithm over other pattern recognition methods. This comparison was based on qualitative criteria (like speed, robustness of outlier, training difficulty, memory requirement and ability to produce measure of uncertainty), as well as quantitative criteria (classification accuracy) [268]. As already described, most EB studies involve a small sample size and a large number of variables. As a consequence, the statistical models developed can often suffer from overlearning or data overfitting [251,269,270]. Hence, a proper validation step is mandatory to increase the quality and robustness of the results. Ideally, this should include both a cross-validation as well as an external validation. Accordingly, in the cross-validation the datasets are randomly divided into \"n\" subsets of equal size. The model is trained using (n-1) subsets and tested using the remaining subset, followed by the calculation of performance measures, such as accuracy, true and false positives, _etc._ The training and testing is repeated till all the subsets are used as test sets. The same performance measures (sensitivity, specificity and accuracy) are then calculated as average values of all the \"n\" tests. It is also possible to use the \"leave one out cross-validation\" (LOO), where \"n\" is equal to the data size. The following external validation should be performed with a totally new dataset obtained from unchallenged samples (also designated as blind samples) data which is equivalent to repetition of the experiments (using a similar sample size) on independent samples obtained from the originally tested population [270]. This strict and rigorous validation protocol is particularly pertinent when using data obtained using e-noses research [250]. In summary, the choice of the best data mining procedure involves most often an option between interpretation and accuracy because more robust data mining methods involve simultaneously a harder interpretation. Moreover, it is crucial build models using an analysis of large number of the samples. This, however, is often not possible, constituting a serious limitation to the discovery of robust biomarkers [270]. This is particularly true for EB VOCs suggested as diseases biomarkers. Invariably, as can be seen from Table 1, there are many EB VOCs that are described as potential biomarkers for different diseases, but they failed when applied in a typical clinical setup. To address this problem, it is desirable to have a standard protocol of guidelines for evaluating an EB analysis study. Such protocol/guidelines have been laid down as STARD guidelines for validating diagnostic tests [271]. These comprise a list of criteria to be used when evaluating a study that proposes a biomarker, before it is used for clinical application [272, 273]. The main goal of this initiative is improve the analytical performance that is important for the characterisation of a disease biomarker, namely the necessary sensitivity to indicate the exclusion of a disease and specificity to distinguish between diseases.