# Effect of COVID-19 on Selected Characteristics of Life Satisfaction Reflected in a Fuzzy Model
###### Abstract
The aforementioned situation significantly affects the health of the population and the effectiveness of the workforce, representing an important scientific, clinical, economic, and social problem. Objectification and reduction of measurement uncertainty will allow not only for a more accurate estimation of the current state, but also forthe prediction of future values and trend measurement to potentially reverse the direction of change from negative (increase in burnout and its severity over time) to positive (stabilization and then decrease in burnout and its severity over time). The historical trajectory, the way it is defined, the diversity of etiologies and symptoms, their personalization, and the imprecision of clinical diagnosis require dedicated tools housed in preventive medicine and lifestyle medicine [28]. One such tool is fuzzy models, already used in the computational modeling of health-related outcomes, including disease diagnosis [9; 10; 11; 29; 30]. A review of the literature showed 6208 publications concerning applications of fuzzy logic in medicine (1974-2022). The literature review showed that fuzzy logic is increasingly being used effectively to diagnose diseases based on symptoms and historical and clinical data, and its popularity is growing. Major areas of fuzzy logic applications in medicine to date include diagnostic testing and/or therapy for diabetes, heart, lung, liver and kidney diseases, breast cancer, cholera, brain tumors, asthma, viral diseases, Parkinson's disease, and Huntington's disease, as well as ophthalmology (iris) and dentistry [29]. Among the important directions indicated for the application of fuzzy logic is the study of celiac disease [29]. The research gap lies in the lack of a fuzzy model reflecting accurately and flexibly the changes in life satisfaction test scores under the impact of the COVID-19 pandemic, and indirectly in the lack of guidelines for further research on more sophisticated models that are technically feasible and clinically useful. Only two of all found articles concern the application of fuzzy logic [11; 31]. The first article presents a new fuzzy algorithm model that extends the interpretability of the scale scores obtained with the Maslach Burnout Inventory (MBI) [11]. The other paper deals with the use of fuzzy logic to design an intelligent fuzzy inference system to assess midwives' burnout levels. It takes into account both findings from the literature on professional burnout and expert knowledge obtained through semi-structured interviews. It incorporates fuzzy rules as more intuitive and easier to understand and modify by users. The study confirms the consistency of midwives' burnout ratings generated by the fuzzy system with the experts' ratings in terms of precision and personalization. This extends the functionality of the system beyond simple assessment also to the study of the interdependence of burnout factors and supports the creation of prevention strategies [8]. Fuzzy logic belongs to artificial intelligence (AI) and, more broadly, computational intelligence (CI) methods [8; 32]. The process of making clinical decisions based on diagnostic test results, patient history, etc., is inherently accompanied by ambiguity and uncertainty, which can be reduced by using fuzzy logic [33]. Uncertainty here does not mean a lack of knowledge, but only a lack of unambiguous accuracy of linguistic description that cannot be assessed by statistical methods or probability calculus. Hence, in the absence of a fuzzy description, the choice would be subject to greater error. Uncertain knowledge works in the vast majority of cases, but not in every case. It is equivalent to expert knowledge--each expert draws knowledge and diagnostic rules from his or her own experience, but this experience need not be the same. The concept of \"minor stress\" or \"moderate pain\", as well as an \"appropriate dose of medication\", may also be experienced or described differently by different patients.