## 4 Conclusions and Future Steps
From the point of view of a typical ML pipeline (see Figure 1), the last steps are the creation of a prediction data model for new data classification and its validation (mostly cross-validation). The step of creating the data model for classification using supervised ML methods is based on pattern recognition and relates to qualitative analysis. The interpretability of the data model for classification is based on the possibility of analyzing spatial distribution data points in a feature space. When initial spectral data were transformed into new independent variables, for example, using PCA, the biochemical meaning of such new variables was not obvious. It means that for approving a data model designed for a medical decision support system, additional ML pipeline steps could be required. These steps consist of establishing the relationships between informative features and key molecular biomarkers, which are associated with differences in informative features of the target group and the control group. In breathomics studies, such molecular biomarkers are the composition of volatile molecules contained in the breath, reflecting the shift in metabolism caused by pathology. Therefore, the step of qualitative and quantitative analysis of exhaled air sample composition is obligatory. In the case of the pattern recognition approach, this step should be implemented after cross-validation of the created data model. In the case of a chemical-based approach, a profile of the most specific volatile molecular biomarker concentrations should be established before the data model creation. To our surprise, only a few works related to medical diagnostics using spectral analysis of exhaled air samples using IR and THz absorption spectroscopy combined with ML were found [8; 58; 59; 60]. The only implementation of pattern recognition and chemical-based approaches on the same date set regarding this task was conducted by us [8]. Two prediction data models were created for acute myocardial infarction diagnosis through exhaled air spectral analysis with IR laser photo-acoustic spectroscopy and ML. The predictive model based on the exhaled air absorption spectrum provided 0.86 of the mean values of both the sensitivity and specificity when linear SVM combined with PCA was used. The created predictive model based on using six VMBs (C\({}_{5}\)H\({}_{12}\), N\({}_{2}\)O, NO\({}_{2}\), C\({}_{2}\)H\({}_{4}\), CO, and CO\({}_{2}\)) provided 0.82 and 0.93 of the mean values of the sensitivity and specificity, respectively, when linear SVM was used. Approving this profile of VMB concentrations requires recovery of the most significant metabolic pathways of pathological processes underlying a target disease. Obviously, such knowledge is very limited at the current stage. It means that bringing breathomics into clinical practices is associated with establishing the key volatile molecular biomarkers in the breath and discovering their metabolic origin. Perhaps the crucial step in these studies can be aimed at finding relationships between molecular biomarkers in breath and in the blood. It means that a more suitable ML pipeline should be transformed into something like the one shown in Figure 5.  
Figure 4: Conventional iterative and ML methods comparison with respect to qualitative/quantitative spectra analysis task. It should be noted that all these additional steps require more complex experimental equipment, time, and expense (see Figure 6). Figure 5: The additional steps in IR-THz spectra processing pipeline for exhaled air samples analysis. Figure 6: Extended analysis of IR-THz spectra for breathomics applications. The bottom layer of the pyramid is common processing resulting in predictive model construction without understanding biochemical mechanisms, which are hidden behind. The second layer (orange) necessary for the pattern recognition approach is to extract concentration profiles for specific metabolites for further analysis.