Fuzzy Expert Systems (FES) for Medical Diagnosis

Sikchi, Smita Sushil; Sikchi, Sushil; Ali, Megat Syahirul Amin Megat

doi:10.5120/10508-5466

Cited by 32 publications

(3 citation statements)

References 107 publications

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“…The findings of this study that are based on medical data and mathematical models, especially fuzzy mathematics, can help to correctly diagnose the disease without losing golden opportunities (3,(12)(13)(14)(15). Therefore, we hope that using the results of this research, the error in the initial diagnosis of diseases is significantly reduced.…”

Section: Objectivesmentioning

confidence: 82%

Diagnostic Differentiation of Crimean Congo Hemorrhagic Fever, Influenza and Bacterial Meningitis by Classic and Fuzzy Mathematics

et al. 2019

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Background: A correct diagnosis of a disease among several diseases with the same clinical symptoms is very important and is a difficult task in medical science. Misdiagnosis of these diseases in the short term causes very high and serious damage to the health of patients and usually results in loss of golden time. Objectives: In this paper, our purpose is to achieve the best conclusion, which contributes to the diagnosis of the critical illness without losing the golden opportunity based on clinical data and using mathematical models, especially fuzzy mathematics. Methods: The data regarding patient's signs and symptoms were collected in the hospitals. We attained the best choice of diseases among the considered options of diseases by using basic fuzzy rules, fuzzy control techniques, fuzzy mathematics and fuzzy systems. To write the basic fuzzy rules, the information that we used was adopted by experts in infectious diseases or data records of patients who reached a definite diagnosis of disease by various tests. Then, by using these rules, the system of mathematical equations was formed. By solving this system, coefficients of a linear equation were estimated witch its values according to the clinical signs of a patient indicates the probability that the patient will be infected with that disease. In this process, the number of patients studied is n not effective. But the more patients are studied, the more accurately the coefficients of the diagnosis equation are obtained. Results: The symptoms of some patients whose disease have been definitely diagnosed were used as inputs of the system of our equations and it was observed that the system's outputs approximately coincide the exact diagnosis of the disease, which indicates that the equations obtained for the diagnosis of diseases are acceptable. Conclusions: The findings of this study can help to correctly diagnose the disease without losing golden opportunities. We hope that using the results of this research, the error in the initial diagnosis of diseases is significantly reduced.

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Section: Objectivesmentioning

confidence: 82%

Diagnostic Differentiation of Crimean Congo Hemorrhagic Fever, Influenza and Bacterial Meningitis by Classic and Fuzzy Mathematics

et al. 2019

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“…Peneliti menggunakan metode logika fuzzy, karena logika fuzzy adalah teknik komputer yang menyediakan metodologi untuk memperoleh solusi dari masalah yang mungkin mengandung berbagai jenis ketidaktepatan dan ketidakpastian, serta logika fuzzy juga membantu mengambil keputusan yang dibantu oleh komputer dalam perhitungan masing-masing individu. Logika fuzzy sudah banyak digunakan dan diterapkan dalam ilmu kedokteran (Sikchi 2013). Dalam logika fuzzy terdapat tiga tahap yaitu fuzzifikasi, inferensi sistem dan defuzzifikasi.…”

Section: Pendahuluanunclassified

Penentuan Level Depresi Mahasiswa Tingkat Akhir Menggunakan Sistem Inferensi Fuzzy dengan Metode Sugeno

Pratiwi

Astuti

2020

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Depression is a disease whose main symptoms are unclear and ambiguous. Depression has a wide influence on health, lifestyle and economic losses. Causes of depression can be seen from sleep disorders, uncomfortable environment, and excessive use of antidepressant drugs. This research determines the level of depression in the final year students who are in the thesis work phase, especially in the mathematics department of FMIPA UNESA, and implements it into fuzzy logic.This type of research is qualitative and quantitative. The inputs us Depression is a disease whose main symptoms are unclear and ambiguous. Depression has a wide influence on health, lifestyle and economic losses. Causes of depression can be seen from sleep disorders, uncomfortable environment, and excessive use of antidepressant drugs. This research determines the level of depression in the final year students who are in the thesis work phase, especially in the mathematics department of FMIPA UNESA, and implements it into fuzzy logic.This type of research is qualitative and quantitative. The inputs used are body mass index, systolic blood pressure and physical health questions. And the output produced is the severity of depression. Data collection techniques used by researchers by asking a number of questions that were asked to 40 final year students. And the processing stage uses the Sugeno fuzzy method. The results of this study can help improve medical services by determining the level of depression in students. This study uses the MATLAB application. The results of this system give an accurate valued are body mass index, systolic blood pressure and physical health questions. And the output produced is the severity of depression. Data collection techniques used by researchers by asking a number of questions that were asked to 40 final year students. And the processing stage uses the Sugeno fuzzy method. The results of this study can help improve medical services by determining the level of depression in students. This study uses the MATLAB application. The results of this system give an accurate value

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“…Solutions known as fuzzy inference systems (FIS) were then designed to map a set of inputs to outputs, using fuzzy logic and fuzzy sets to define vague conditions. These characteristics allowed fuzzy logic and fuzzy set theory to find a lot of uses in various areas, such as medicine [5]- [7], computer security [8]- [14], networking [15], [16], aeronautics [17], stock trading [18], and many others [4], [19]- [22].…”

Section: Introductionmentioning

confidence: 99%

BDFIS: Binary Decision Access Control Model Based On Fuzzy Inference Systems

Regateiro

Pereira

Aguiar

2019

International Conferences on Software Engineering and Knowledge Engineering

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Access control is a ubiquitous feature in almost all computer systems, and as data becomes more and more of an important asset for organizations, so do the associated access control policies. However, with the increase in the amount of data being produced, e.g. in IoT and social networks, the interest in simpler access control is increasing as well since more subjects (public, researchers, etc.) are now requesting access to it. Defining the exact conditions to allow each subject to access the data can be difficult, especially when vaguely defined conditions such as "expertise of a researcher" come into play. Fuzzy Inference Systems (FIS) allow to process these vague conditions and enables access control mechanisms to be more easily applied. The contribution of this paper lies in showing how a FIS can be used to output binary access control decisions (grant/deny) and what are the differences in the inference process that stems from restricting the output to these two output values.

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Fuzzy Expert Systems (FES) for Medical Diagnosis

Cited by 32 publications

References 107 publications

Diagnostic Differentiation of Crimean Congo Hemorrhagic Fever, Influenza and Bacterial Meningitis by Classic and Fuzzy Mathematics

Diagnostic Differentiation of Crimean Congo Hemorrhagic Fever, Influenza and Bacterial Meningitis by Classic and Fuzzy Mathematics

Penentuan Level Depresi Mahasiswa Tingkat Akhir Menggunakan Sistem Inferensi Fuzzy dengan Metode Sugeno

BDFIS: Binary Decision Access Control Model Based On Fuzzy Inference Systems

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