ABSTRACT
Internet is almost a necessary facility and tool to solve daily life problems in every field life. Whether at the individual level or national and international level sale purchase of any kind of object has always been of much importance, especially after Corona Pandemic, when online business is at its peak. Because of the enhancement of online sales and purchases, various businessmen are looking for suitable internet websites for their businesses, and the selection of the most suitable internet websites is one of the multi-attribute decision-making (MADM) dilemmas. Thus, in this script, we take benefits of three various concepts that are Bonferroni mean (BM) operator which is a significant technique to catch the interrelatedness among any number of inputs, Dombi operations which are based on Dombi t-norm and t-conorm and the ability to create an aggregation procedure more flexible because of the parameter, bipolar complex fuzzy set (BCFS) which is an outstanding model for tackling two-dimensional information with negative aspect and interpret bipolar complex fuzzy (BCF) Dombi Bonferroni mean (BCFDBM), BCF weighted Dombi Bonferroni mean (BCFWDBM), BCF Dombi geometric Bonferroni mean (BCFDGBM), and BCF weighted Dombi geometric Bonferroni mean (BCFWDGBM) operators. After ward, in this script, for tackling MADM dilemmas in the setting of BCFS, we investigate a MADM procedure based on the investigated operators and solve a MADM dilemma (selection of a suitable internet website for businessmen). Further, to display the superiority and efficiency of our work, we compare our approach and operators with a few current approaches and operators. Author
ABSTRACT
Background: Diabetes mellitus has been considered a significant risk factor for morbidity and mortality for COVID-19.1 HbA1c levels are often used as a marker of poor glycaemic control and are one way of diagnosing pre-diabetes as well as diabetes.2,3 We tried to explore whether HbA1c levels could be an independent risk factor for mortality and morbidity in patients with positive coronavirus (SARS-COv-2) swabs. Methods: This was a retrospective multicentre study of coronavirus swab positive patients who had a recent HbA1c test. Their demographic data, medical history, COVID-19 swab and laboratory results, and final outcomes were analysed. Patients were divided into three groups;HbA1c in normal (group 1), pre-diabetic (group 2) and diabetic (group 3) ranges. Data were analysed using JASP and statistical computation using a χ2 test. Results: A total of 1,226 patients had SARS-CoV-2 RNA identification swabs between 10 February 2020 and 1 May 2020. A cohort of 120 of these patients had positive swab results and recent HbA1c results. Mortality rates for group 1 (normal HbA1c) and 3 (diabetic HbA1c) were relatively higher than group 2 (pre-diabetic HbA1c). Among group 2, female patients had greater mortality, perhaps because of fewer male patients, although overall co-morbidity was less (4/120 (3.33%) in group 2 compared with 18/120 (15%) in group 1 and 14/120 (11.66%) in group 3. Overall, 36/120 (30%) patients died and 84/120 (70%) survived. Survival curves after analysis of data showed that increasing HbA1c levels were associated with poorer outcomes across all groups. Analysis was significant with p=0.003. Conclusions: HbA1c levels in this study were an independent marker of increased risk of mortality in COVID-19 swab positive patients. The findings are statistically significant (p=0.003). Increased co-morbidities at normal HbA1c seem to have a contributing role in enhanced mortality.