key: cord-292623-mxdlii77
authors: Arji, Goli; Ahmadi, Hossein; Nilashi, Mehrbakhsh; A. Rashid, Tarik; Hassan Ahmed, Omed; Aljojo, Nahla; Zainol, Azida
title: Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification
date: 2019-09-26
journal: Biocybern Biomed Eng
DOI: 10.1016/j.bbe.2019.09.004
sha: 
doc_id: 292623
cord_uid: mxdlii77

This paper presents a systematic review of the literature and the classification of fuzzy logic application in an infectious disease. Although the emergence of infectious diseases and their subsequent spread have a significant impact on global health and economics, a comprehensive literature evaluation of this topic has yet to be carried out. Thus, the current study encompasses the first systematic, identifiable and comprehensive academic literature evaluation and classification of the fuzzy logic methods in infectious diseases. 40 papers on this topic, which have been published from 2005 to 2019 and related to the human infectious diseases were evaluated and analyzed. The findings of this evaluation clearly show that the fuzzy logic methods are vastly used for diagnosis of diseases such as dengue fever, hepatitis and tuberculosis. The key fuzzy logic methods used for the infectious disease are the fuzzy inference system; the rule-based fuzzy logic, Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy cognitive map. Furthermore, the accuracy, sensitivity, specificity and the Receiver Operating Characteristic (ROC) curve were universally applied for a performance evaluation of the fuzzy logic techniques. This thesis will also address the various needs between the different industries, practitioners and researchers to encourage more research regarding the more overlooked areas, and it will conclude with several suggestions for the future infectious disease researches.

Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification show that the fuzzy logic methods are vastly used for diagnosis of diseases such as dengue fever, hepatitis and tuberculosis. The key fuzzy logic methods used for the infectious disease are the fuzzy inference system; the rule-based fuzzy logic, Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy cognitive map. Furthermore, the accuracy, sensitivity, specificity and the Receiver Operating Characteristic (ROC) curve were universally applied for a performance evaluation of the fuzzy logic techniques. This thesis will also address the various needs between the different industries, practitioners and researchers to encourage 1.

An infectious disease can be defined as a situation that is created by the incursion of a human body by harmful agents which will in turn hurt the body and may spread to other people as well [1] . Infectious diseases are the major cause of illness and death for any given population [2, 3] . Human Immunodeficiency Virus infection and Acquired Immune Deficiency Syndrome (HIV/AIDS), Severe Acute Respiratory Syndrome (SARS), H1N1 influenza and Poliomyelitis are examples of an infectious disease [3, 4] . While globally the incidence of the infectious disease varies greatly between countries, in the 21st century, the main incidences are hand-foot-mouth disease, Hepatitis B, and Tuberculosis [3] . These diseases have a significant impact on the global health and economies [5] . Despite numerous valuable achievements regarding the procedures for preventing and controlling of various diseases, infectious diseases remain a massive threat to a population's well-being [6] . Aspects such as the increasing in antimicrobial resistance, increase in population and environmental changes are important in an infectious disease transmission [7] . Of 57 million mortality reported per year throughout the world, 14.9 million were related to infectious diseases; which represent more than 25% of the overall deaths [3] .

Additionally, clinical diagnosis and detection of the contaminated population are critical elements in controlling and supervising an infectious disease [8] . For the diagnosis of different diseases, there are different essential criteria that should be interpreted by the caregivers [9] . In a clinical situation, an analysis physician combines a patient's medical history, clinical symptoms, physical examination and laboratory findings [10] . Due to the elusive and complex nature of clinical decisionmaking, they may be accomplished with unwanted errors [11] .

In other word, medical diagnosis is an error-prone process, which occurs as the result of logical thinking [10] . Mathematical models are essential means for demonstrating the cause and effect relationship and evaluating the evidence for decisiveness regarding infectious diseases [12] . Computer-aided methods have the potential for examining the complexity of an infectious disease dynamics [11, 12] . Computational tools are essential for understanding epidemiological patterns in a disease outbreak [13] . For handling the uncertainty of decision-making, studies have tried to explain the decision-making process in a medical setting by using the Boolean or Binary structure [11] .

Fuzzy logic is considered as a vigorous technique for modelling ambiguity in medical practice [14] . In medical field, most medical concepts are fuzzy [15, 16] . These concepts usually are difficult to formalize and measure [17] . Fuzzy logic is making a decision in an inaccuracy, uncertainty and incompleteness environment [14] . Fuzzy methods deal with the classes whose boundaries are unclear and elusive [18] . Fuzzy logic concepts were introduced in 1965 [10] . The medical field was one of the first fields in which fuzzy theory was implemented [19] . Fuzzy classes are given a degree of membership that is intermediary between 0 and 1 [18] .

Some of the cases for a medical use of the fuzzy set theory are the MYCI, INTERNIST and DOCTORMOON applications [10, 20] . Therefore, for these application methods, different types of research have been conducted in the disease diagnosis field. So, the major objective of the current study is to examine the researches in which fuzzy logic techniques have been applied in infectious diseases so to determining its trends and methods, through the processes of conducting a Systematic Literature Review (SLR). The current thesis is structured in the fallowing order. Section 2 exemplifies the procedures or the methodology of the current SLR, Section 3 will present a complete report of the current systematic review. Section 4 will be dedicated to the discussion of this research. Section 5 will be dedicated to the implications of this study, its future research prospects and to the limitations of this evaluation and the last section will be the conclusion.

We carried out an SLR to define the influence of using the fuzzy logic methods in infectious diseases. The three main questions of this SLR are as follow: (RQ1) which domain of an infectious disease was more interesting in past researches? (RQ2) Which fuzzy methods were more dominant in infectious disease data analysis? (RQ3) Which performance evaluation methods were more frequently applied in previous reviews? 

For the gathering of the relevant information from all the eligible articles, a data extraction method was used for getting the detailed answer of the research questions.

(RQ1) Which domain of an infectious disease was more interesting in past researches?

(RQ2) Which fuzzy techniques were more dominant in an infectious disease data analysis?

(RQ3) Which performance evaluation procedures were more frequently applied in the previous reviews?

To answer the research questions and to produce the extracted data, numerous methods were used. Generally, a narrative combining approach to answering different research questions was applied. Furthermore, various visualization techniques such as tables and charts were used according to the research questions.

Current part is dedicated to the outcome of the current SLR. First, we will demonstrate an overall description of the outcome of selecting the appropriate studies; and then, all the obtained outcomes will be categorized for each study questions independently.

By searching for the four aforementioned databases, the 372candidate paper was removed as shown in Fig. 1 . Then, based on using the exclusion conditions, 162 studies were excluded. The other 210 articles were investigated meticulously to choose relevant studies. By reviewing the title, abstract and the keywords, merely papers that have at least one of the inclusion measures were used. Eventually, 40 papers were used to get an answer to the research questions and data extraction for this Methodical Evaluation. Fig. 2 is showing the distribution of eligible papers per year. According to the diagram below, the frequency of papers relevant to the use of fuzzy logic in an infectious disease was roughly stable during the first 5 years. However, as shown in the next chart, there is a persistent trend in the number of Finally, the spread of the relevant articles based on a journal or conference, has been explained in Table 1 . Based on the obtained results Expert Systems with Applications journal was used by researchers for publishing their studies in the 9.6 percent of instances.

RQ1: Which domain of an infectious disease was more interesting in past researches?

Articles related to the precise domain of fuzzy methods application in an infectious disease, which have been considered from the past section, are summarized in the following section. Fig. 3 illustrates the distribution of numerous fuzzy techniques that have been used in an infectious disease data analysis. Based on this figure, the results revealed that the critical application domain of the fuzzy methods in an infectious disease were relevant to dengue fever (15% of papers), hepatitis and tuberculosis (10% of papers). In addition, the other papers were related to diseases such as pulmonary infection (7.5% of papers), Urinary Tract Infections (UTIs), Human Immune deficiency Viruses (HIV) and Meningitis (5% of papers). For all the other diseases, there is one paper per each disease (2.5% of papers).

Appendix A, Table A1 lists eligible articles according to the specific type of disease, their objective, their inward and outward variables and findings.

As shown in Fig. 3 , the distribution of the different fuzzy methods in various disease data analysis was as fallows. In articles published for dengue fever, 33.33% of the papers applied fuzzy set theory and the Adaptive Neuro-Fuzzy Inference System (ANFIS) method in data analysis while 16.6% of the papers used association rule mining. 50% of selected papers that related to hepatitis, employed Neuro-Fuzzy classifier as a fuzzy technique, while the other 50% were devoted to fuzzy inference system and Fuzzy Decision Support System (FDSS). Furthermore, in tuberculosis studies, 50% of the papers used rule-based fuzzy logic and 50% of the papers applied the Gaussian-Fuzzy neural network method.

RQ2: Which fuzzy techniques were more dominant in an infectious disease data analysis? 

Fuzzy inference systems are increasingly becoming more predominant in the area of fuzzy logic methods where 15% of the selected papers in the current SLR are related to this particular method. Fuzzy inference systems are used for the processes of mapping the inward variables to appropriate outward [21, 22] . Fuzzy inference process incorporates three key concepts: the membership functions, the fuzzy set operations, and the inference procedures [23] . A fuzzy inference system can be divided mainly to four portions as follows: fuzzification, weighting, assessment of inference procedures, and the de-fuzzification [24] .

DragoviT et al. utilized the fuzzy inference system to decide the probability of having peritonitis in patients. By using this system, the fuzzy rules enable the automation of the clinical decision making process in an imprecise and complicated conditions [25] . Urinary Tract Infections (UTIs) are considered amongst one of the utmost predominant bacterial infections.

Ibrisimovic et al. has suggested a FIS fuzzy model, to provide the necessary support that the caregivers need for explaining the aftermath of a microscopic urine analysis. To create the model's various variables such as the Colony Forming Units (CFU), White Blood Cells (WBC) and the Red Blood Cells (RBC) as well as the turbidity of urine specimen used for inward variables, and the risk of a UTI as an outward variable. The end result has revealed that the use of the fuzzy methods simplifies and secures the clarification of urine analysis [26] .

Putra and Munir proposed a method for diagnosis of measles, German measles and varicella. The data for those diseases were used because of the similarity of their infection mechanism and symptoms. A built fuzzy inference system b i o c y b e r n e t i c s a n d b i o m e d i c a l e n g i n e e r i n g 3 9 ( 2 0 1 9 ) 9 3 7 -9 5 5 takes in the inward variables that represent the possible symptoms that may appear in each disease. Cough, runny nose, sore throat, conjunctivitis, Koplik's spot, diarrhea, headache, swollen neck or ear, loss of appetite, malaise, pimples/crust skin, joint pain used as inward variables. The application effectively identified 19 out of 25 accurate diseases throughout its testing stage [27] .

The ANFIS method was proposed by Jang and its notions then were used in other fields [22] . This method works by setting a list of features by using an amalgam of learning rules which will incorporate the back-propagation incline in error digestion and a tiniest squares method. This method can be implemented as the basis for creating a set of IF-THEN guidelines with suitable association of functions to brand the inward and outward variables [22] . ANFIS method has been notably successful in disease diagnosis in the past few years. As an example, the major groups of hepatitis in human beings are hepatitis A, hepatitis B and hepatitis C with the main symptoms being malaise (a common sick feeling), fever, and muscle pain, loss of appetite, nausea, vomiting, diarrhea, and jaundice. Viral hepatitis disease can be diagnosed by blood test analysis and interpretations. Dogantekin et al. developed an ANFIS system to be used for hepatitis diagnosis. The precision achieved in this automated system of diagnosis was at about 94.16% [28] .

Faisal et al. conducted a research using an adaptive neurofuzzy inference system to diagnose a dengue fever. The general precision of the developed method is 86.13% with its sensitivity being at 87.5% and its specificity being at 86.7% [29] . This method was used in the Campisi research as well to determine the amount of the risk factors of an infection disease in relationship with Oral Candidiasis (OC) [30] .

Additionally, Shariati has recommended a method of diagnosis for both hepatitis and thyroid diseases. The researchers in this study compared the outcome of the ANFIS technique with the Support Vector Machine (SVM) and the artificial neural networks techniques. Then, they demonstrate that this technique had an improved outcome in being a precise diagnosis in comparison with the previous techniques [31] .

The information in a fuzzy rule-based system is typically represented by using an IF-THEN statement. Rule-based Fuzzy logic includes two portions: the precursor portion are the relevant conditions which are known as the inward variable(s), and the subsequent portion which expresses the outward variable(s).

Mamdani and Sugeno are two different types of fuzzy rulebased systems. In the Mamdani technique, the precursor, as well as the subsequent section, comprises fuzzy statements that reveal the value of the variables, while in Sugeno method, the subsequent portion displays a nonlinear affiliation among both the inward and outward variables [32, 33] . Because of the complex nature of the numerous diseases, this technique can improve the infectious disease diagnosis and the treatment of such diseases as HIV and tuberculosis. Based on this, Sloot et al. efficiently used rule-based fuzzy logic to uncover the drug-resistance in HIV patients [34] .

Furthermore, Semogan et al. has created a clinical decision supporting system based on the fuzzy logic and the rule-based model that determine different classes of tuberculosis to assess respiratory diseases. In this system variables such as cough, cough duration, body temperature, fever duration, sputum discoloration, nose sputum, afternoon chills, night sweats, weight loss, and loss of appetite have been used in the diagnosis [35] . b i o c y b e r n e t i c s a n d b i o m e d i c a l e n g i n e e r i n g 3 9 ( 2 0 1 9 ) 9 3 7 -9 5 5

The Fuzzy Cognitive Map (FCM) is a modelling technique that defines the connections between concepts such as variables, the inwards and the outwards by the methods of previous knowledge and experience. Kosko has introduced FCM in 1986. FCM is usually used to demonstrate the cause and effect relationship between the different concepts in a given system. FCMs are used in numerous fields such as in engineering, error detection and medicine [36, 37] . In this regard, Mei et al. has applied FCM to describe how factors such as people's emotions and cognition functions influence each other to create epidemic infection [38] . FCM was also used by the Mago et al. research to develop the required knowledge-based structure used for recognizing the specific causes and the symptoms of meningitis disease in children. This system can be implemented as a dependable instrument in supporting the physician's to better their decision making processes [39] .

RQ3: Which performance evaluation techniques were more frequently applied in the previous reviews?

Performance evaluation procedures are among the furthermost significant indicators for determining the quality of the artificial intelligence techniques [40] . Overall, performance evaluation procedures are classified into two main types; the first being the single scaler techniques and second being the graphical techniques.

The sensitivity, specificity and the accuracy indicators are grouped into the single scaler techniques. Receiver Operating Characteristic (ROC) curve, cost-line, and lift graph are clustered together in graphical methods. Anyhow, graphical methods cannot simply be clarified and analyzed as single scalar method [41] [42] [43] . Fig. 5 indicates the most noticeable performance evaluation indicators used in eligible papers.

For the current SLR, most qualified studies did not use any kind of indicators for analyzing the performance of the fuzzy techniques. Among the fuzzy techniques, ANFIS and FIS techniques were evaluated by single scaler techniques and graphical evaluation techniques.

The major objective of this SLR was to select and examine the various studies relevant to the employment of the fuzzy logic techniques in an infectious disease. In this regard, 40 studies were selected and analyzed from an original number of 372 candidate studies. This section is dedicated to the discussion of the major findings of this study.

RQ1: Which domain of an infectious disease was more interesting in past researches?

In last few years, it has been obvious from the results of analytic studies conducted in our research that there is a growing interest in studying the relevance of the fuzzy logic techniques for an infectious diseases diagnosis applications. The studies have shown that there is an increase in the number of published articles from 5% in 2005 up to 17.5% of the evaluated papers in 2017. This growing trend may prove the popularity of the fuzzy techniques between different academic papers and its valuable effects in identifying the infectious disease trends. Additionally, 13 different fuzzy logic techniques have been used in the evaluated papers. From a medical point of view, in Fig. 3 we found that the main application domain of fuzzy logic in infectious disease was related to dengue fever, hepatitis and tuberculosis respectively. The other noticeable outcome that we have found was the b i o c y b e r n e t i c s a n d b i o m e d i c a l e n g i n e e r i n g 3 9 ( 2 0 1 9 ) 9 3 7 -9 5 5 fact that the evaluated papers were very diverse in terms of disease types.

Because of the big impact on global health by infectious diseases, determining the different features of these diseases are a valuable source for improving our knowledge and ability to predict how a disease will spread in a population [2, 3] . The ability to comprehend and control an infectious disease can be obtained by the usage of mathematical models [44] . The newly arising infectious agents such as HIV, the Severe Acute Respiratory Syndrome (SARS), the Mid-Eastern respiratory syndrome (MERS) coronaviruses; the West Nile Virus; the Nipah virus; the drug-resistant pathogens; novel influenza A strains and the Ebola virus outbreak were considered as big challenge to health in the recent century [6] . Using computeraided diagnosis techniques like the fuzzy logic technique can be useful in determining the main factors associated with the infectious diseases occurrence and epidemics.

Regarding this, in another field of research in relationship to the vaccination strategies for infectious diseases it has been shown that infectious diseases such as influenza have a seasonal pattern in which case mathematical techniques such as the fuzzy logic techniques is considered a vital instrument in the process of forecasting the viral development from one year to another. This technique can also deliver the required scientific confirmation which will help us decide the amount of vaccine treatment, its efficiency, its financial costs and its pattern of contact in any given population [6, 45, 46] . It has been shown that the fuzzy based techniques have an essential part in the determination of infectious disease outbreaks. As an example for that, we have the HIV outbreaks, these techniques can identify at what time a viral transmission has occurred, its outbreak stage and the sexual behavior pattern of the specific population in which a disease is spreading. Moreover, the infectious diseases annihilation and elimination techniques dictate that the transmission process itself has to be the target not the disease. Regard this, the data sources are typically scattered and the collective effect of the numerous control techniques are complex and are dependent on the rate of transmission [47] .

RQ2: Which fuzzy techniques were more dominant in an infectious disease data analysis?

As shown in Fig. 4 , there is a various number of fuzzy techniques employed in the chosen papers. Based on this studied section we can mostly classify the fuzzy techniques into four main classes; the fuzzy inference system, the rulebased fuzzy logic, ANFIS and the fuzzy cognitive map. The fuzzy inference system and the ANFIS technique were used as shown in 17.5% and 12.5% of candidate papers, respectively. FIS is based on the 'IF-THEN' rules and it can be used to predict the behavior of a various uncertain situations [48] . As seen in the distribution of the studied articles, we can say that fuzzy techniques are efficient means for modelling unclear disease conditions such as in the case of transmissible disease diagnosis.

Additionally, in the last few years, there has been substantial interest amongst the researchers to apply the ANFIS technique in the case of an infectious disease. ANFIS combines the positive effects of both ANN and FIS in an influential tool for disease diagnosis. This technique does not require excessive knowledge in the modelling and training system. These techniques are usually valuable for the situation, which are in many cases complicated, with a nonlinear behavior pattern. This work approach has created a relationship between the inward and the outward features by the means of the neurons [49] .

RQ3: Which performance evaluation procedures were more frequently applied in the previous reviews?

Performance evaluation procedures are usually employed as a valued method in determining the quality of the numerous fuzzy techniques [40] . As shown in Fig. 5 in the current SLR, amongst the single scaler techniques, the sensitivity, specificity and the accuracy are frequently utilized in the evaluation of a developed technique. Additionally, in graphical evaluation techniques, the ROC curve was the other measure of performance evaluation that was used in the adequate papers. These indicators are to be considered significant when reporting and assessing a diagnostic technique. Scientists have to provide suitable information about the sensitivity, specificity, and the projected values when describing a computer based diagnostic technique end results and this information must contain how those metrics are concluded and also what are its appropriate interpretations [43, 49] .

Although those indicators are promising in qualified studies, nonetheless, they typically generate an inadequate image of the indicators performance and thus it is possible to lose a certain amount of valuable information. Moreover, it is possible that the employment of a single-scaler technique will not identify the full scope of a performance assessment. Therefore, a comprehensive and dependable assessment must reflect all the numerous parts of performance distinctive quality.

In this methodical review, the studies related to the employment of the fuzzy logic techniques in an infectious disease were assessed, and depending on the acquired outcomes, we can notice an interest amongst the researchers regarding this specific field of research. In last few years, a large number of the transmissible diseases that were supposed to be eliminated have made a comeback. Certain factors such as manufacturing, agricultural practices, wars, changes in lifestyles, development and urbanization, and environmental change are all effective in the appearance and reappearance of an infectious disease [50] . This SLR's result demonstrates that even though these are the most of the infectious diseases that were investigated, but there is still more area that needs to be covered. Nevertheless, more work should be carried out on the appearance and reappearance diseases domains such as H1N1, SARS, Zoonosis and the Rift valley fever.

Internationally, there is a lack of an integrated framework for reporting infectious disease [51] . Additionally, an infectious diseases information system has an inadequate support for data analysis and generating predictive techniques centered on artificial intelligence. An integrated analytical framework that offers functions as in a progressive data analysis capability and a visualization support is of a critical importance [51, 52] . There is serious need for creating an atmosphere for collecting, distributing, reporting, assessing, and picturing the infectious disease data and to provide support for decisionmaking tools regarding disease prevention, recognition, and controlling [53, 54] .

Infectious disease observation and controlling has demanded an interdisciplinary work. To have the ability to achieve those objectives, the employment of Geographic Information Systems (GIS), three-dimensional information analysis, machine learning and visualization applications and techniques became a must. Because of the significance of the infectious diseases at the global level, it is essential to simultaneously develop an incorporated infectious disease dataset and to make the specialized analytical and diagnostic methods all at the same time.

Additionally, there was slight conversation in the incorporated literature about three-dimensional information analysis for an infectious disease occurrence. Three-dimensional information evaluation techniques may be useful in determining the concentration pattern of a disease occurrence and to make the required association from the determined patterns to the measureable procedures [55, 56] .

Furthermore, the social networks information study has facilitated the evaluation of the association amongst the population in a particular social setting. Methods that correlate spatial and social network data analysis are unusual but have the capacity to promote the determination of a spreading progress of an infectious disease [57] . Or we can say, the deployment of a unified spatial and social network structure to define the spreading of an infectious pathogens in a given populace will allow insights into both the understanding to the disease method of distribution and the possible process associated with the observed patterns. Since there are various studies regarding social media information analysis, we recommend the use of this dataset for the prediction of an infectious disease epidemic. This research has its limitations. Even with the use of a broad search approach, some of the publications regarding the fuzzy logic deployment in an infectious disease could not be recovered, as in the case of grey literature and reports that were not published in surveyed digital databases, which we have reviewed. Thus, it is recommended that additional SLR papers should be carried out to go through the other noticeable databases. Additionally, some studies did not report clearly on the performance assessment technique. In conclusion, only the English language publications were included, therefore future studies could be expanded to incorporate relevant papers which are published in other languages.

In this study we have identified, classified, and defined the use of the fuzzy logic techniques in infectious diseases. 40 studies were scrutinized and the main conclusions can be briefed as follows: (1) the key application field of the fuzzy logic in an infectious disease was related to dengue fever, hepatitis and tuberculosis, (2) amongst the fuzzy logic techniques fuzzy inference system, rule-based fuzzy logic, ANFIS and fuzzy cognitive map are commonly used in many studies, and (3) the major performance evaluation indicators such as the sensitivity, specificity, and the accuracy the ROC curve is employed. In addition, this study highlights the absence of an integration of infectious disease information systems in order to provide a valuable datasets in this domain. Additionally, using machine learning and visualization applications for information analysis is essential.

Even though in the current SLR there is a diversity of infectious diseases that are investigated, there is only one article per each disease. There is additional need to use the fuzzy logic methods for infectious disease detection and prediction. It appears that one of the causes for a limited number of relevant articles to infectious diseases is in the difficulty in obtaining adequate research data.

Finally, we expect that a mounting number of infectious diseases datasets will be mostly obtainable in the forthcoming years because of raising collaboration amongst medical practitioners and researchers and that this would lead to additional studies of the machine learning techniques that can be useful in this regard. 

The generated results are more reliable for the prediction of TB in patients. 35 Langarizadeh [82] 2014 Meningitis Rule-based fuzzy logic This system is used to distinguish between bacterial and aseptic meningitis, by using fuzzy logic.

Gram stain, White blood cell (WBC) count in cerebrospinal fluid (CSF), Percentage of polymorphonucleocytes in CSF, CSF protein, CSF/ serum glucose ratio, WBC count in blood, percentage of blood neutrophils, Blood C-reactive protein (CRP), and platelet (Plt) count.

Bacterial and aseptic meningitis

This suggested system has shown great efficiency in regards to its ability to differentiating between the bacterial and aseptic meningitis. 36 Omisore [83] 2017 Tuberculosis Genetic-Neuro-Fuzzy Proposing a Genetic-Neuro-Fuzzy for the diagnosis of Tuberculosis. 

The study has proposed FCMs for determining the symptoms and causes for meningitis patients. b i o c y b e r n e t i c s a n d b i o m e d i c a l e n g i n e e r i n g 3 9 ( 2 0 1 9 ) 9 3 7 -9 5 5

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Clinical aspects of influenza A (H1N1) in HIVinfected individuals in São Paulo during the pandemic of

Global trends in emerging infectious diseases

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Ecology of increasing diseases: population growth and environmental degradation

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Profiles of multiple causes of death related to HIV/AIDS in the cities of Sao Paulo and Santos, Brazil

Fuzzy-trace theory, risk communication, and product labeling in sexually transmitted diseases

Fuzzy logic and exponential smoothing for mapping implementation of dengue haemorrhagic fever in Surabaya

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A boolean consistent fuzzy inference system for diagnosing diseases and its application for determining peritonitis likelihood

A fuzzy model to predict risk of urinary tract infection

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Automatic hepatitis diagnosis system based on Linear Discriminant Analysis and Adaptive Network based on Fuzzy Inference System

Adaptive Neuro-Fuzzy Inference System for diagnosis risk in dengue patients

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Comparison of anfis neural network with several other ANNs and support vector machine for diagnosing hepatitis and thyroid diseases

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A mathematical model with control to analyse the dynamics of dengue disease transmission in urban Colombo

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Assessing airborne transmission of foot and mouth disease using fuzzy logic

Automating the clinical stools exam using image processing integrated in an expert system

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A fuzzy expert system for distinguishing between bacterial and aseptic meningitis

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