# Reliability Evaluation of the Factors That Influenced COVID-19 Patients' Condition
## 6 Discussion
These methods analyse the frequency aspects of human error dominantly and predict the human error. Some of the HRA methods (for example, Cognitive Reliability and Error Analysis Method (CREAM) [54]) analyse the human cognitive aspects, the errors' causes rather than their frequency. However, the quantitative assessment in the HRA methods needs a reducing of the uncertainties of the initial data. Expert judgment is used extensively to compensate for a lack in data. However, the expert data have a specific uncertainty, too, which should be taken into account [55]. The proposed method for the building of the structure function based on incomplete data allows forming of the mathematical representation of the system with two factors of uncertainty, as incompletely specified and ambiguous, which is characteristic of expert data. The application of the proposed method in HRA can be efficient. It is illustrated by the manually calculated example presented in this paper. Another possible application of the proposed method can be in healthcare safety [20; 22]. One needs to note that the conception of health safety is correlated with the human factor evaluation [14; 15; 16]. There are special methods of human factor analysis in medicine, which have been developed based on typical methods of HRA: Observational Clinical Human Reliability Analysis (OCHRA) [55] and Healthcare Failure Mode Effect Analysis (HFMEA) [56]. Both of these methods are based on qualitative assessment of the human factor. The application of the proposed method allows quantitative assessment of the human factor in medicine. The proposed method can be used not only to analyse the human factor, but also to construct the mathematical model in the form of the structure function (1) of any other model for which the initial data is not completely specified [34] or such data is heterogeneous [57]. Examples of such systems can be the evaluation of protection against terrorist attacks [58], critical infrastructure [59], drone fleet analysis [60], and other. The proposed method can be useful for structural reliability [61]. The next aspect of the proposed method application is analysis of the result of the classification, which can be used in problems of Data Mining. In future investigations, we plan to develop the proposed method based on the studies in Data Mining, too. In this paper, FDT was used as classifier for the induction of the structure function. This classifier is often explored in medical applications because it allows a good visualisation of the decision [22]; but, according to studies in [39; 62; 63], the Neural Network and Deep Leaning methods are effective and have the best accuracy for many applications. The Deep Leaning-based method has been used for COVID-19 detection based on X-ray image analysis [64], natural language processing [65], and epidemiology forecasting [66]. The Deep Learning and Neural Network methods can be effective for a reliability analysis. These and other classification methods in Data Mining have been well investigated [37; 44; 57; 59; 67]. There are many methods for the induction of different classifiers [14; 44; 67]. However, the problem of the analysis of the influence of the input attributes into the result of the classification has not been investigated sufficiently. The interpretation of the developed classifier as the structure function permits to quantify the influence of the input attribute to the output class of the decision. Such an investigation can be more effective if the structure function will be constructed for the MSS (it is introduced in Section 2.1). For example, the methods for the MSS analysis based on the existing structure function of MSS are presented in [32; 42; 68]. So, the proposed method can be used in applied problems of reliability engineering and data mining.