## 4 Conclusions and Future Steps  
The pattern recognition approach requires the extraction of independent informative features, which must provide the best sensitivity and selectivity of a created prediction data model to make accurate medical decisions. Implementation of a pattern recognition approach in breathomics using ML methods does not require solving Equation (3) directly. Instead, an ML algorithm is trained to distinguish spectral patterns of various classes. Figure 3: Suggested optimal ML pipeline. In the case of IR and THz absorption spectroscopy, these patterns are absorption spectra profiles of exhaled air samples. Convolutional neural networks are a golden standard for pattern recognition [34; 62]. The advantage of such an approach is that it does not rely on the pure substances' spectra linear superposition model, which is why, in the most interesting experimental spectra processing case, the intermolecular interactions of nonlinear elements of different molecule types are considered. Some recent works show outstanding results in joint analysis using CNN and specially designed interpretability methods like Grad-CAM [34]. Grad-CAM can provide results that are similar to Local Interpretable Model-Agnostic Explanations (LIME) and the Shapley Additive explanations (SHAP) technique in a contrastive metric sense [90] but underperforms when additional layers are placed between the last convolution and the classification layer. Other shortcomings are the drop in localization efficiency with multiple object instances and the poor ability to capture the whole object. These limitations can be overcome by the application of Grad-CAM++ and Integrated Gradients [91; 92]. Even so, a full understanding of the extremely complicated deep neural network decision-making process is far from being perfect [93; 94]. Often, the application of supervised ML methods requires large-volume training sets, especially deep neural networks. A way to obtain them without using expensive spectrometers is to generate model spectra using spectral databases (NIST, HITRAN, GEISHA, etc.). However, using synthetic spectra for training and validation data sets leads to data model prediction accuracy decreasing when it is tested on new experimental data. Hybrid datasets combine both advantages of the means of data collection: the simplicity of obtaining synthetic spectra and considering the set of physical effects. Therefore, ML methods offer a means of interaction between synthetic and experimental data [61]. The chemical-based approach differs only by using the VMB concentrations profile as an informative features vector (see Figure 1). The step is related to quantitative analysis. The chemical-based approach is based on Equation (3)'s solution [95]. In the case of using conventional iterative methods (like MCR), the objective function is minimized by coefficients \(c_{i}\) prediction variation (by movement along an objective function surface). The exhaled air sample always belongs to \"grey analytical systems\" [27]. This task can be solved by using supervised ML methods like DNNs [95] and methods like HAMAND and RSC [8; 89]. With respect to supervised ML methods, a similar process of optimal \(c_{i}\) values estimation takes place, but with the help of similarity metric(s) relative to the spectra contained in a training dataset. That is, the supervised ML method compares a new spectrum with those obtained earlier and looks for the most similar to the one that was submitted as input. All feasible solutions of Equation (3) (sets of \(c_{i}\)) form a surface with many local extrema. Finding a universal optimal solution is a hard task, and ML methods are specially designed for that, outperforming conventional methods. A comparison of conventional and ML techniques is presented in Figure 4. Therefore, only a chemical-based approach provides interpretability of medical decisions due to the possibility of discovering VMBs of a pathological process.