key: cord-0889812-l0i3a5uh authors: Das, Rakhal; Mukherjee, Anjan; Tripathy, Binod Chandra title: Application of Neutrosophic Similarity Measures in Covid-19 date: 2021-12-02 journal: Ann DOI: 10.1007/s40745-021-00363-8 sha: ba7daa2e67e5651e6cb889b789f8ee48b83ab1b8 doc_id: 889812 cord_uid: l0i3a5uh The contemporary situation of the world is very pathetic due to the spread of COVID-19. In this article, we have prepared a decision making model on COVID-19 pandemic patients with the help of the neutrosophic similarity measures. The model is to predict the COVID-19 patents for testing positive and testing negative. The decision making is based on the testing result of the COVID-19 cases. We have used the neutrosophic similarity measure theory and the distance function. We have used the C-programming for finding the result of the suspected patients. The World Health Organization (WHO) announced a pandemic crisis due to COVID-19 on March 11, 2020. COVID-19 has a variety of effects on various persons. The COVID disease manifests itself in six different ways. The majority of infected patients will experience mild to moderate sickness and will be able to recover without the need for hospitalization. COVID-19 was discovered for the first time in December 2019 in Wuhan, China. On January 30, 2020, the World Health Organization designated the outbreak a Public The six clusters of symptoms outlined in the study are: Flu-like with no fever: Headache, loss of smell, muscle pains, cough, sore throat, chest pain, no fever. Flu-like with fever: Headache, loss of smell, cough, sore throat, hoarseness, fever, loss of appetite. Gastrointestinal: Headache, loss of smell, loss of appetite, diarrhea, sore throat, chest pain, no cough. Severe level one, fatigue: Headache, loss of smell, cough, fever, hoarseness, chest pain, fatigue. Severe level two, confusion: Headache, loss of smell, loss of appetite, cough, fever, hoarseness, sore throat, chest pain, fatigue, confusion, muscle pain. Severe level three, abdominal and respiratory: Headache, loss of smell, loss of appetite, cough, fever, hoarseness, sore throat, chest pain, fatigue, confusion, muscle pain, shortness of breath, diarrhea, abdominal pain. The first level, "flu-like with no fever," is associated with headaches, loss of smell, muscle pains, cough, sore throat, and chest pain. Patients at this level have a 1.5% chance of needing breathing support such as oxygen or a ventilator. The second type, "flu-like with fever," includes symptoms like loss of appetite, headache, loss of smell, cough, sore throat, hoarseness, and fever. Researchers say about 4.4% of patients at this level needed breathing support. Patients with the third type, simply described as "gastrointestinal," do not have a cough as part of their illness. Instead, they experience headaches, diarrhea, loss of smell, loss of appetite, sore throat, and chest pain, and about 3.3% needed breathing support. Lahita referred to the following three clusters of COVID-19 as the "really severe types." In type four, or "severe level one," patients experience fatigue along with headache, loss of smell, cough, fever, hoarseness, and chest pain. Patients at this level needed breathing support at a rate of 8.6%. Type five, "severe level two," includes the symptoms of type four along with loss of appetite, sore throat, and muscle pain, and is mainly distinguished by confusion. "That means you don't know where you are or where you live, whether you are in or out of the hospital, who your relatives are," Lahita explained. "That is very scary." Almost 10% of patients at that level need breathing support. How the coronavirus almost killed a healthy woman with "no normal symptoms". The most severe type of COVID-19 is referred to as "severe level three, abdominal and respiratory," and has all the above symptoms along with abdominal pain, shortness of breath, and diarrhea. Nearly 20% of these patients need breathing support. Lahita said "Those are the severe level threes who wind up on a ventilator, and then it is touch-and-go as to whether they survive the infection entirely". The U.K. researchers have observed that only 16% of patients with type one COVID-19 required hospitalization, compared with nearly half of the patients with type six. Patients in the severe clusters also tend to be older or with pre-existing conditions and weakened immune systems, compared to those in the first three. We hope that it would help to predict the types of care patients with COVID-19 might need, and give doctors the ability to predict which patients would fall into which category. Data Science and related technologies are critical in the fight against pandemics, such as the 2003 severe acute respiratory syndrome coronavirus (SARS-CoV), COVID-19, to enable governments and health managements figure out the best preparation and response. Big data, data mining, machine learning, and a variety of other technologies can be used to swiftly and effectively evaluate data in order to track and regulate COVID-19's spread over the world [17] [18] [19] [20] . Big data is currently a hot topic among researchers, engineers, health-care executives, and administrators [26] . Several academics have made extensive use of data mining techniques to uncover hidden information in large datasets [19] [20] [21] . The results obtained from the study by Liu et al. [19] can be linked to India and other countries, which will be valuable in understanding the virus transmission patterns among the population of different age-groups. In this paper we have prepared a C-programming by which is can compare suspected patents with the COVID-19 patents a prediction based on the expert data. In this section we procure some definitions and examples which will be used in rest of the article. Definition 2.1 [6, 7] Let U be an initial universe and E be a set of parameters. Let P(U) denotes the power set of U and A ⊆ E. Then the pair (F, A) is called a soft set over U, where F is a mapping given by F:A → P(U). From philosophical point of view, the neutrosophic set takes the value from real standard or non-standard subsets of] − 0, + 1 [But in real life application in scientific and engineering problems it is difficult to use neutrosophic set with value from real standard or non-standard subset of] − 0,1 + [. Hence we consider the neutrosophic set which takes the value from the subset of where T A (x) is called truth-membership function, I A (x) is called an indeterminacymembership function and F A (x) is called a falsity membership function. Definition 2.3 [6] Let U be the universal set and E be the set of parameters. Let A ⊆ E and P(U) be the set of all neutrosophic sets of U. Then the collection (F, A) is called neutrosophic soft set over U, where F is a mapping given by F: A → P(U). Definition 2.4 [32] Let U is a space of points (objects), with a generic element in U. A single value neutrosophic set (SVN-set) A in U is characterized by truth membership function T A , a indeterminacy-membership function I A and a falsity-membership Definition 2.5 [7] Let U be an initial universal set, E be a set of parameters and A ⊆ E. Let SVNS(U) denotes the set of all interval value neutrosophic subsets of U. The collection (F,A) is termed to be the interval valued neutrosophic soft set over U, where F is a mapping given by F: A → SVNS(U). In this section we define hamming and Euclidean distances between two single valued neutrosophic sets and proposed similarity measures based on these distances. COVID-19 disease is a severe issue that has affected many people around the world. The following are some of the state's most common symptoms. A. Fever is a temperature measurement in degrees Celsius. Coughing that is dry, C. Exhaustion, E. Loss of taste or smell, D. Headache F. shortness of breath or difficulty breathing. We create a neutrosophic soft set based on those symptoms, and by establishing a distance function, we create a C-programming that allows us to determine whether a patent Covid is positive or not. In this case, we'll use conventional Covid-19 patent data to calculate the distance between two regular patents; if the distance is zero, the patent is considered Covid positive. If the distance is great, we can assume the patents will have other issues. If a patent has other issues, the percentage can be calculated using the neutrosophic distance. N H D(A, E) 1 3mn m j 1 n i 1 T A x j (e i ) − T E x j (e i ) + I A x j (e i ) − I E x j (e i ) + F A x j (e i ) − F E x j (e i ) where, μ is the true membership value for covid-19 η is the indeterminacy element. 6. Normalized Neutrosophic Distance The percentage of possibility for Covid-19 positive is T*100%,. The percentage of doubt for Covid-19 positive is I*100%,. The percentage of possibility for Covid-19 negative is F*100%,. In case of neutrosophic distance if T*100%,. ≥ I*100%,. + F*100%, then the decision will be in favor of true membership value. We have defined three types of distance function for the finding the distance between two patents where one patents have covid-19 and other one will measure by the distance function and what the percentage of covid positive occur. If the distance lies in the interval [0, 0.4] then we consider both has the similar condition if the distance lies (0.4, 0.55) then cannot decided it must take under observation and if distance lies in [0.55, 1] then we consider the patents may have opposite decision. First we construct a decision making method based on similarity measure of two neutrosophic sets. It is a medical diagnosis problem on COVID-19. Similarity measure of two neutrosophic soft sets based on Normalize Hamming distance, and Normalized Euclidian distance can be applied to estimate the possibility that an ill person having certain symptoms. He /She is suffering from COVID-19 or not. We construct a neutrosophic soft set for the disease and another neutrosophic soft set for ill person. Then we find the similarity measure of these two neutrosophic soft sets. If the similarity measure is less than or equal to 0.4 (which can be fixed by a medical expert person) then we conclude that the person is possibly suffering from COVID-19. If the similarity measure is greater than 0.4to 0.55 then the person is not possibly suffering from the disease COVID-19 but need observation. similarity measure is greater than or equal to 0.55 then the patents are free from COVID-19. The steps of this algorithm are: Step 1 A neutrosophic soft set element is Constructed for disease over the universe U. It is based on medical expert. Step 2 A neutrosophic soft set (G, B) is Constructed over the universe U for a patient. Step 3 Calculate the Hamming distance between (F, E) and (G, E). Step 4 Calculate similarity measure of (F, E) and (G, E). Step 5 Estimate result by using the similarity. Here we are providing an example of a decision-making method in a single-valued neutrosophic soft set setting using a similarity measure. To handle that pandemic situation, select an ideal case (a patient who was suffering from COVID-19). By calculation of similarity measure of any suspected patient with an ideal case to the diagnosis of the COVID-19. Through this technique, doctors can take fast a decision to diagnosis the disease and create a helping environment in medical diagnosis. In this case, let us consider a patient who was suffering from COVID-19. Using the COVID-19 patient data we will decide for a set of elements of new patients. P 1 , P 2 , P 3 and universal set U of Experts. Let U {x 1 , x 2 , x 3, x 4 } be the universal set and E {e 1 , e 2 , e 3 , e 4 , e 5 , e 6 } be the set of parameters. Let (A, E), (P 1 , E), (A, P 1 )and (A, P 3 ) are four neutrosophic soft sets over U. Here A is the known person we have to find out the distance of P i from A. The tabular representations of A and P i 'sare as follows: e 1 e 2 e 3 e 4 e 5 e 6 x 1 < 1, 0, 0 > < 1, 0, 0 > < 1, 0, 0 > < 1, 0, 0 > < 1, 0, 0 > < 1, 0, 0 > x 2 < 1, 0, 0 > < 1, 0, 0 > < 1, 0, 0 > < 1, 0, 0 > < 1, 0, 0 > < 1, 0, 0 > x 3 < 1, 0, 0 > < 1, 0, 0 > < 1, 0, 0 > < 1, 0, 0 > < 1, 0, 0 > < 1, 0, 0 > In this paper we established a c-programming for finding the distances between two single valued neutrosophic soft sets. We use different distances function for similarity measure between two single valued neutrosophic soft sets. A programming is developed using for decision making problem. An example is given to demonstrate the possible application of proposed C-program in COVID-19 patients. The article usable for other decision making problems also, which is quite relevant to neutrosophic soft set. Note The work is executed by the C-programming. The programming is given in the Appendix below. Intuitionistic fuzzy sets Interval valued intuitionistic fuzzy sets Distances between interval-valued intuitionistic fuzzy sets Intuitionistic fuzzy soft sets Fuzzy sets and its applications, Shanghai Science and Technology Press Soft set theory−first results Similarity measure of intuitionistic fuzzy soft sets and their decision making Similarity measures between vague sets and between elements The entropy and similarity measure of interval valued intuitionistic fuzzy sets and their relationship Similarity measure of soft sets On similarity measures of fuzzy soft sets On distance based similarity measure between intuitionistic fuzzy soft sets Similarity in soft set theory Similarity measures of interval-valued fuzzy soft sets Similarity measures for interval-valued intuitionistic fuzzy soft sets and its application in medical diagnosis problem Entropy distance and similarity measure of fuzzy sets and their relations Monitoring novel corona virus (COVID-19) infections in India by cluster analysis Culture vs Policy: more global collaboration to effectively combat COVID-19 What are the underlying transmission patterns of COVID-19 outbreak? An age-specific social contact characterization Identification of the most significant risk factor of COVID-19 in economy using cosine similarity measure under SVPNS-environment Neutrosophic bipolar vague soft set and its application to decision making problems Internet of things, real-time decision making, and artificial intelligence Multi-criteria group decision making model using single-valued neutrosophic set Joint modeling of Longitudinal CD4 count and time-todeath of HIV/TB Co-infected patients Introduction to business data mining Optimization based data mining: theory and applications Effect of high intensity intermittent training on heart rate variability in prepubescent children Clinical implications of dysregulated cytokine production The politics of nature. In: Smith J (ed) The rise of modern genomics Future of health insurance Neutrosophic fuzzy matrices and some algebraic operations A unifying field in logics, neutrosophy: neutrosophic probability, set and logic Code Availability Not applicable. Conflict of interest Authors declare that they have no conflict of interest.