# Reliability Evaluation of the Factors That Influenced COVID-19 Patients' Condition
## 6 Discussion  
A new method for mathematical representation construction based on uncertain data is proposed in this paper. The mathematical representation is constructed as the structure function of the system, which permits to use all known methods of the system analysis based on its representation in the form of a structure function. The novelty of the proposed method is the interpretation of the structure function as a classifier. It allows using a method for classifier induction (in our case FDT) for the structure function development. Figure 4: Fuzzy Decision Tree (FDT) for the investigated data. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(x_{1}x_{2}x_{3}\) & \(x_{4}x_{5}\) & \(0\) & \(0\) & \(1\) & \(1\) \\
**0** & **1** & **0** & **1** & **0** & **1** \\ \hline
1 1 0 & 0 & 0 & 0 & 0 \\
1 1 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The structure function of the system for evaluation of COVID-19 patients. Any classifier can be defined by the decision table. This table is considered as the truth table of the structure function. The analysis of the influence of the input attributes on the classification result is implemented by the importance analysis, in particular, based on SI (9a) or (b). The input attribute with the highest value of SI has the greatest impact on the result. This aspect of the application of the proposed method was used in an analysis of COVID-19 patients: we analysed and evaluated the influence of concomitant disease, age, and intubation. The initial data for this analysis have been simplified. We did not use all the data presented in the investigated data ([[https://www.gob.mx/salud/documentos/datos-abiertos-152127](https://www.gob.mx/salud/documentos/datos-abiertos-152127)]([https://www.gob.mx/salud/documentos/datos-abiertos-152127](https://www.gob.mx/salud/documentos/datos-abiertos-152127)) (accessed on 28 October 2020), [[https://www.kaggle.com/tanmoyx/covid19-patient-precondition-dataset?select=covid.csv](https://www.kaggle.com/tanmoyx/covid19-patient-precondition-dataset?select=covid.csv)]([https://www.kaggle.com/tanmoyx/covid19-patient-precondition-dataset?select=covid.csv](https://www.kaggle.com/tanmoyx/covid19-patient-precondition-dataset?select=covid.csv)) (accessed on 18 January 2021)). In this paper, only the technical aspects of the proposed method to analyse the influence of the input attributes on the classification result were considered. This study was performed without detailed consultation with doctors. Therefore, we do not publish results that could be interpreted as real recommendations. Similar studies based on real data were carried out and published in the work [54]. The evaluation of the proposed method accuracy, specificity, and other metrics (Table 8) shows that it has acceptable parameters and can be used for such evaluations. A comparison of some classifiers was implemented too. According to the result of this comparison, the FDT-based classifier for the considered problem has the best metrics. One of the advantages of the proposed method is the different aspects and areas of its application. The application of this method should be considered as minimal according to two aspects. The first of them is its application in reliability engineering. The possibility of the structure function building based on incomplete and uncertain data extends the applications of this mathematical representation. One of the possible application areas of this method can be the analysis of the human factor. Human Reliability Analysis (HRA) is one of the important parts of reliability engineering. The HRA methods permit to analyse failures in human communications and evaluate human failure and the impacted causes [51]. As a rule, the initial data in HRA is incomplete and uncertain. Therefore, the HRA method provides a qualitative analysis. Examples of such methods are the Human Error Assessment and Reduction Technique (HEART) [52] and Success Likelihood Index Method (SLIM) [53].