

SHORT COMMUNICATION 

Year : 2022  Volume
: 12
 Issue : 4  Page : 334340 

Classification of COVID19 Individuals Using Adaptive NeuroFuzzy Inference System
Mohammad Dehghandar, Samaneh Rezvani
Departments of Applied Mathematics, Payame Noor University, Tehran, Iran
Date of Submission  23Jul2021 
Date of Decision  14May2022 
Date of Acceptance  10Jun2022 
Date of Web Publication  10Nov2022 
Correspondence Address: Mohammad Dehghandar Department of Applied Mathematics, Payame Noor University, PO Box 369719395, Tehran Iran
Source of Support: None, Conflict of Interest: None
DOI: 10.4103/jmss.jmss_140_21
The COVID19 has become an important health issue in the world and has endangered human health. The purpose of this research is to use an intelligent system model of adaptive neurofuzzy inference system (ANFIS) using twelve variables of input for the diagnosis of COVID19. The evaluation of the model was performed using the information of 500 patients referred to and suspected of the COVID19. Three hundred and fifty people were used as training data and 150 people were used as test and validation data. Information on 12 important parameters of COVID19 such as fever, cough, headache, respiratory rate, Ctchest, medical history, skin rash, age, family history, loss of olfactory sensation and taste, digestive symptoms, and malaise was also reported in patients with severe disease. ANFIS identified COVID19 in accuracy, sensitivity, and specificity with more than 95%, 94%, and 95%, respectively, which indicates the high efficiency of the system in the correct diagnosis of individuals. The proposed system accurately detected more than 95% COVID19 as well as mild, moderate, and acute severity. Due to the timeconstraint, limitations, and error of COVID19 diagnostic tools, the proposed system can be used in highprecision primary detection, as well as saving time and cost.
Keywords: Accuracy, adaptive, COVID19, diagnosis, neurofuzzy
How to cite this article: Dehghandar M, Rezvani S. Classification of COVID19 Individuals Using Adaptive NeuroFuzzy Inference System. J Med Signals Sens 2022;12:33440 
Introduction   
At present, with the advancement of science, the importance and complexity of the decisionmaking process are undeniable and increasing. Since the decisionmaking process is influential in the next process, you must be careful to do so. Therefore, it seems necessary to need information systems to help the decisionmaking process. On the other hand, with the addition of large and uncertain variables, complexity and disruption in decisionmaking increase.^{[1]} Due to the interference of variables, physicians can use the information system by extracting specialized knowledge from clinical experts and specialists and entering it into their knowledge base. Therefore, in today's world, the use of medical information systems is very important in improving the decisionmaking process of physicians.^{[2]} COVID19 is a new virus called SARSCOVID2, which was first reported to Chinese citizens on December 31, 2019, and approved by the World Health Organization.^{[3]} In a very short time, the virus went beyond the epidemic and became a pandemic.^{[4]} COVID19 belongs to the family of Respiratory Syndrome Middle East and Respiratory Syndrome viruses.^{[5]} Among the most widely used modeling methods, we can refer to artificial neural networks (ANNs), which are based on the learning model of the human nervous system. Neural networks and fuzzy systems play a valuable role in the diagnosis of diseases. Sanchez pointed to medical science as a fuzzy relationship between symptoms and disease, and Adlassnig carefully described it.^{[6],[7]} In 2015, Dehghandar et al. used fuzzy theory to rank the temperature of febrile diseases in traditional Iranian medicine. The authors presented their proposed model by presenting eleven input variables, five output variables, and 32 rules.^{[8]} In another study, Dehghandar et al. used fuzzy theory to determine the cause of the body pulse mask with the help of pulse parameters in traditional Iranian medicine. They presented their proposed model assuming ten input parameters, three output parameters, and 25 rules.^{[9]}
Numerous studies have been performed on the use of fuzzy inference systems and neural networks associated with COVID19, some of which are briefly mentioned. Painuli et al. using the input variables of fever, cough, age, diabetes, travel history, respiratory problems, flu, hearing problems, olfactory loss, body aches, and sore throats designed a fuzzy expert system.^{[10]} Nitesh Dhiman and Sharma proposed a fuzzy inference system for the detection and prevention of COVID19 using the gaussian membership function and six input variables.^{[2]} Melin et al. presented the overall effect of the neuralfuzzy network model for predicting time series, COVID19 using a Mexican case study. They first trained the neural network to predict the data and then provided the results of the three parameters to the fuzzy system.^{[11]} In another study, Shaban et al. diagnosed COVID19 based on a fuzzy inference engine, and deep neural network.^{[12]} Abeer Fatima et al. demonstrated the internet of thing's ability to intelligently monitor COVID19 using a fuzzy inference system.^{[13]}
Abbas Khan et al. presented internetbased hierarchical inference systems, objects, and medicine for the diagnosis of COVID19 using inputinput variables in two layers.^{[4]} In another study, Dehghandar et al. designed a fuzzy expert system for the diagnosis of COVID19 using twelve input variables and extracting 29 rules using a Lookup Table.^{[14]}
In another study, Hossam et al. used an ANN, support vector machine, and decision tree to classify Ctchest images of people with COVID19 and nonCOVID19.^{[15]} Using adaptive neurofuzzy inference system (ANFIS), Deif et al. used white blood cells and platelet counts to diagnose COVID19 disease.^{[16]}
Current research, uses an adaptive fuzzy inference system based on artificial neural and twelve variables such as fever, cough, headache, respiratory rate, Ctchest, Medical history, skin rash, age, family history, loss of olfactory sensation and taste, digestive symptoms, and malaise are presented. The system determines the severity of a patient with COVID19 by comparing the symptoms of COVID19 declared by the World Health Organization. Timely diagnosis of the disease and use of the proposed system at any time and place will increase the speed, of decisionmaking and reduce errors in nondiagnosis by physicians. Individuals can evaluate their symptoms using this model and, if the results are positive, begin quarantine without endangering the lives of others. In summary, the research performed and its comparison with the present research are given in [Table 1].  Table 1: The research was done and their comparison with the present research
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Materials and Methods   
The present study is a diagnostic study that, in addition to identifying COVID19, also detects its severity with high accuracy, which is of particular importance in COVID19 screening. To design the neuralfuzzy system, we used the information of 500 patients. The following is a brief description of the ANFIS. The ANFIS is used to study phenomena with nonlinear equations that, while preserving the advantages of the fuzzy inference system, also con be taught.^{[17]}
A fuzzy system consists of four main parts, which include fuzzifier, inference mechanism, knowledge base, and defuzzifier.^{[1]} Fuzzy systems cannot learn independently, and the ability to learn is enhanced by combining neural networks. After receiving the inputs, the system estimates the output, then compares the output with the actual output, and corrects the deviations by methods. This cycle continues as long as the estimated deviation of the estimated outputs from the actual output has the least acceptable deviation. One of the important features of the ANFIS is that its output follows the Sugeno method, a firstorder polynomial of input variables as a result. In this method, to approximate the function f, a set of ifthen fuzzy rules of type TagakiSugenoKang (TSK) is designed for the number of m vectors including the number of n inputs and one output. If the output of a fuzzy system is a combination of inputs, it is called a TSK system and its rules are as follows:^{[18],[19]}
wherein
Therefore, fuzzy sets are expressed as follows:
And if then the weighted average output for the number of r rules is as follows:
wherein
If the fuzzy sets are as a Gaussian membership function and in the interval [α_{i},β_{i}]then each x_{i}∈[α_{i},β_{i}] of the domains are defined as A_{i} existing in Eq. 2 and the degree of the membership function is nonzero, That is . The Gaussian membership functions of each fuzzy set A^{L}∈{1, 2,..., r}are considered according to Eq. 4:^{[1]}
Where m_{i} and σ_{i} are the centers and variances of the adjustable and the set of parameters of the antecedent section, respectively. [Figure 1] shows the general diagram of the TSK ANFIS architecture.^{[20]}  Figure 1: The TSK ANFIS architecture. Layer 1: Each node represents a linguistic label. Here, The Gaussian membership functions of each fuzzy set according to Eq. 4, Layer 2: Every node is a fixed node whose output is the product of all the incoming signals from Layer 1, Layer 3: Every node is a fixed node labeled with “N,” Layer 4: Every node L in this layer is a node function y^{L}w̄^{L} where w̄^{L} is the output of Layer 3, Layer 5: The single node in this layer is a fixed node. It computes the overall output as the summation of all incoming signals
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The proposed system includes various factors such as fever, cough, headache, respiratory rate, Ctchest, medical history, skin rash, age, family history, loss of olfactory and taste, digestive symptoms, and malaise determines while they are directly involved in the diagnosis and treatment of the disease. In this research, to convert real numbers to fuzzy sets, Gaussian and trapezoidal membership functions have been used. For system training, 70% of the data is equivalent to the information of 350 patients. In addition, 15% of the data equivalent to the information of 75 patients were used to test the model and 15% of the data equivalent to the information of 75 patients were used to validate the model.
For designing ANFIS models, there are three fuzzy inference system structures namely grid partition, subtractive clustering, and fuzzy cmeans.^{[21]} The main difference between the two methods is in how they determine membership functions. In the Grid Partitioning method, the type and number of membership functions are determined by the user with the input information vector. However, in the SubClustering method, the type of membership functions is determined by the ANFIS model according to the specificity of the input information vector and their classifications.
The fuzzy sets used to describe the behavior of the system are obtained by experts or by trial and error and should be considered to cover input and output variables. To improve the performance of the system, membership functions, variables, inputs, dependents, and factors with common factors were combined and considered as a variable. In this research, the method of SubClustering and reduction of Gaussian functions and trapezoidal membership function with eight input variables and 30 epochs has been used, which leads to the production of the desired output, namely COVID19 detection. An output variable is divided into three modes: mild, moderate (be cautious), and acute (quarantine and the need for special attention), indicating the severity of patients with COVID19. [Table 2] shows the input and output variables of patient identification status.
Confusion matrix
In general, confusion matrices are used in disease classification and diagnosis systems to evaluate the success and efficiency of these systems.^{[15]} The criteria used in this view are as follows:
TP: All sick people who have been correctly diagnosed.
FP: All people with the disease who have been mistaken for healthy.
TN: All healthy people who have been correctly diagnosed.
FN: All healthy people who have been mistaken for a patient.
Network accuracy for test and validation data is obtained from Eq. 5 as follows:
The performance evaluation of the algorithms described above has been done using different criteria based on the sensitivity and detection perspective. The sensitivity index means the ratio of the number of sick people to the total number of people from Eq. 6 and can be calculated as follows:
Also, the specificity index means the ratio of the number of healthy people to the total number of people is calculated from Eq. (7) as follows:
The proposed system according to Equation (5) with an accuracy above 95% determines the severity of COVID19 in the patient. In addition, the sensitivity of the system according to Eq. 6 is more than 94% and the specificity of the system designed according to Eq. 7 is more than 95%, which indicates the high efficiency of the system in diagnosing severe COVID19 in the affected person. [Table 3] shows the data confusion matrix using the proposed system.
Results   
Using the collected data sets, a new model was developed for COVID19 diagnosis based on ANFIS capabilities. A computer with an Intel (R) Core (TM) i54300 processor, 8 GB of RAM, and MATLAB R2014b software was used to implement the ANFIS. In ANFIS, the software plots the training data into a graph and then delivers the same data to the system. The ANFIS estimates the output based on this data and displays it on the same chart to be comparable to the training data.
To train the system, the information of 350 patients was given to the system in the form of a 350line matrix (each row related to a patient's information) with 8 columns for eight input variables along with the evaluation results of each case in a separate column. Test and validation data were randomly selected and not used in the training process. For this purpose, to evaluate and efficiently the system of test and validation data in the form of two matrices of 75* 9 was considered. After loading the training, testing, and validation data in the graphical interface, the SubClustering method was used. The membership Function of Fever is defined as a trapezoid membership function and other input variables are in the form Gaussian membership function.
The results of the ANFIS in the testing and validation stages are shown in [Figure 2] and [Figure 3], respectively.  Figure 2: Results for adaptive neurofuzzy inference system for test data
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 Figure 3: Results for adaptive neurofuzzy inference system for validation data
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[Figure 2] and [Figure 3] show that the model has been able to understand the ruler model well and accurately in the training, testing, and validation stages. Thus, in [Figure 2], the network output data and the test data are matched. Similarly, in [Figure 3], ANFIS outputs with validation data show high system efficiency.
[Figure 4] shows an overview of the user interface of the ANFIS for the detection of COVID19. Fuzzy logic interprets the rules well, but cannot obtain the rules automatically. Therefore using ANFIS 26 rules were determined, some of which are shown in [Table 4].  Figure 4: Part of the user interface of adaptive neurofuzzy inference system for diagnosis of COVID19
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[Figure 4] shows a system designed for an elderly patient with a lowgrade fever and cough, whose lungs are not infected and who have headaches, skin rashes, and lethargy, and who has lost their sense of smell and taste, who has no gastrointestinal symptoms or underlying disease and breathing rhythm is abnormal and no one in his family has the disease. In this case, the output shows a mild condition, which means that the patient has mild Covid19 disease, which predicts the designed system to be true.
[Figure 5] shows the inputoutput behavior of the system as a threedimensional representation based on the two input variables Ctchestfamily history and age. Similarly, [Figure 6] shows a threedimensional representation based on the two input variables Ctchestfamily history and fever with Gaussian and trapezoidal membership functions.  Figure 5: Surface view for two parameters: Ctchestfamily history and age
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 Figure 6: Surface view for two parameters: Ctchestfamily history and fever with Gaussian and trapezoidal membership functions
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Due to the importance of the COVID19 virus, which can endanger human health, in this study, an assistive system was presented for use in medical universities, and hospitals, as well as error reduction in the diagnosis of the COVID19 virus. COVID19 was designed. In the present study, an intelligent system using an ANFIS by the information of 500 patients suspected of COVID19 was suggested. The proposed system is designed with the information of 350 patients referred to treatment centers and is tested and validated on the information of 150 patients. Twelve variables: fever, cough, headache, respiratory rate, Ctchest, medical history, skin rash, age, family history, loss of olfactory and taste, digestive symptoms, and malaise used for diagnosis and prediction of COVID19. In most of the previous research, which is summarized in [Table 1], fewer variables were used to diagnose COVID19 disease. The use of further input variables increases the accuracy and success of the system, indicating that the present research is more comprehensive. The proposed reference system,^{[14]} using the information of 375 patients, identifies 93% accuracy of COVID19 disease and also the sensitivity of the system is more than 95% and the specificity of the system is more than 87%. In the present study, with the increase in the number of patients to 500 patients, the ANFIS method, which uses neural network learning algorithms and fuzzy logic to design a mapping between input and output space and has good capabilities in training, construction, and classification, has been used and is more accurate than previous research.
According to the results of this ANFIS, the accuracy of the proposed system was above 95%, the sensitivity of the system more than 94%, and the specificity of the system more than 95% were able to detect Covid19 with mild, moderate, and acute ratings. The results provided by this system are very promising and can be used with high accuracy as well as saving time and cost.
Data availability
The data used to support the findings of this study are available from the corresponding author upon request.
Financial support and sponsorship
None.
Conflicts of Interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]
[Table 1], [Table 2], [Table 3], [Table 4]
