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![Diagram showing the relationship between spinal nerve roots and vertebrae [27].](profile/Saeede-Rahimi-Damirchi-Darasi/publication/324683974/figure/fig10/AS:753484697726976@1556656159277/Diagram-showing-the-relationship-between-spinal-nerve-roots-and-vertebrae-27.jpg)
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![Figure 2. Type-2 FLS [29].](profile/Saeede-Rahimi-Damirchi-Darasi/publication/324683974/figure/fig11/AS:753484697710592@1556656159335/Type-2-FLS-29_Q320.jpg)
The majority of people have experienced pain in their low back or neck in their lives. In this paper a type-2 fuzzy rule based expert system is presented for diagnosing the spinal cord disorders. The interval type-2 fuzzy logic system permits us to handle the high uncertainty of diagnosing the type of disorder and its severity. The spinal cord diso...
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... next most common is disc C4/5 and disc C7-T1 may rarely be herniated [26]. Figure 1 represents the relationship between spinal nerve roots and vertebrae [27]. ...
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... module of herniated disc is activated due to pain in the leg and low back or in the arm and neck. Antecedents' variables of fuzzy rules of severity of pain in the leg/low back and arm/neck are shown in Figure 10. Figure 11 presents some of the rules and membership functions of variables of this module. ...
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... variables of fuzzy rules of severity of pain in the leg/low back and arm/neck are shown in Figure 10. Figure 11 presents some of the rules and membership functions of variables of this module. ...
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... module of mechanical pain is activated because of pain in the low back or pain in the neck. Antecedents' variables of fuzzy rules of severity of pain in the low back and neck are shown in Figure 12. Figure 13 shows some of the rules and membership functions of variables of this module. ...
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... module of mechanical pain is activated because of pain in the low back or pain in the neck. Antecedents' variables of fuzzy rules of severity of pain in the low back and neck are shown in Figure 12. Figure 13 shows some of the rules and membership functions of variables of this module. ...
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... module of spinal stenosis is activated because of pain in either legs or both arms. Antecedents' variables of fuzzy rules of severity of pain in both legs and both arms are shown in Figure 14. of the rules and membership functions of variables of this module. ...
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... system could dnd the exact location of the problem between lumbar and cervical discs. The domain of the system in diagnosing the herniated disc is represented in Figure 16. To accelerate the search for the exact location of the disorder, the system asks some questions to investigate the symptoms based on prevalence of the disorder. ...
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... questions have a major role in nding the exact location and ensuring the patient's malingering. Variables of rules for the herniated disc in the low back and neck are represented in Figure 17. ...
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... diagnose these disorders, the system investigates some of their symptoms to distinguish them from mechanical disorder. Antecedents of fuzzy rules of this module are represented in Figure 18. ...
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... distinguish these disorders, the system investigates some of the uncommon symptoms of the vascular problem. Antecedents of fuzzy rules of this module are represented in Figure 18. ...
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Expert systems, a type of artificial intelligence that replicate how experts think, can aide unskilled users in making decisions or apply an expert's thought process to a sample much larger than could be examined by a human expert. In this paper, an expert system that ranks financial securities using fuzzy membership functions is developed and appl...
Citations
... To classify and labeling, some supervised learning algorithms such as Ada boost, SVM, KNN, NB, and PNN were used [29]. Damirchi-Darasi et al. [30] presented an expert system for diagnosing spinal cord disorders. They used type-2 fuzzy logic system to handle the high uncertainty of diagnosing the type of disorder and its severity [30]. ...
... Damirchi-Darasi et al. [30] presented an expert system for diagnosing spinal cord disorders. They used type-2 fuzzy logic system to handle the high uncertainty of diagnosing the type of disorder and its severity [30]. Damirchi-Darasi et al. [30] showed that the accuracy of their proposed system is acceptable. ...
... They used type-2 fuzzy logic system to handle the high uncertainty of diagnosing the type of disorder and its severity [30]. Damirchi-Darasi et al. [30] showed that the accuracy of their proposed system is acceptable. Karimizadeh, Vali, and Modaresi [31] suggested a method to decide about Pseudomonas Aeruginosia infection status in cystic fibrosis patients based on their respiratory sound. ...
... Dalam analisis sistem dari implementasi sistem pakar dibutuhkan basis regulasi maupun basis pengetahuan yang berupa informasi tepat, utuh, serta terstruktur dengan jelas, sehingga proses inferensi dapat berjalan sebagaimana mestinya [11]. Maka digunakan metode forward chaining yang merupakan bagian dari banyak tahapan deduksi yang berawal dari sejumlah validitas yang dideteksi. ...
... Pada tahap ini dilakukan implemetasi dari aplikasi website sistem pakar diagnosa penyakit tulang belakang yang sudah dibuat, setelah pada tahap sebelumnya sudah melakukan analisis dan perencanan sistem baik dari use case, 9,11 9,3 9,02 9,4 8,89 ...
... Dalam proses ini digunakan metode Perbandingan Eksponensial, guna membantu menentukan urutan prioritas alternatif keputusan pada kriteria jamak [11] . Dimana guna membandingkan akurasi antara kombinasi metode forward chaining dan certainty factor dengan metode forward chaining dan naïve bayes pada diagnosa penyakit tulang belakang. ...
In the field of medicine, especially orthopedics, there are several types of spinal diseases including scoliosis, lordosis, kyphosis, and spondylosis. Therefore, an expert system is needed to diagnose spinal diseases. Based on this problem, we performed an exponential comparison of the two hybrid methods. With the final value of the combined forward chaining & naive bayes method of 8.89, and 9.40 the highest final value of the combined forward chaining method and certainty factor. So that in this research, forward chaining and certainty factor methods are used which are designed based on a website, using Sublime Text 3 programming tools and PHP and MySQL as databases. From the results of application testing and manual calculations with 30 sample data, it was concluded that 7 users or about 23% entered the level of confidence in the possibility of developing spinal disease and 23 users or 77% of the total testing stated at the level of confidence in the probability of developing spinal disease with a value of the highest confidence of 88.5% in spondylosis disease.
... Patel et al. [51], Anand et al. [52], Mishra et al. [53] and Sundararaman et al. [54] proposed Asthma fuzzy diagnosis systems. Damirchi-Darasi et al. [55] proposed fuzzy rule-based expert system for the diagnosis of spinal cord disorders. Kadhim et al. [56] suggested a fuzzy expert system for back pain diagnosis. ...
The coronavirus (COVID-19) pandemic has caused severe adverse effects on the human life and the global economy affecting all communities and individuals due to its rapid spreading, increase in the number of affected cases and creating severe health issues and death cases worldwide. Since no particular treatment has been acknowledged so far for this disease, prompt detection of COVID-19 is essential to control and halt its chain. In this paper, we introduce an intelligent fuzzy inference system for the primary diagnosis of COVID-19. The system infers the likelihood level of COVID-19 infection based on the symptoms that appear on the patient. This proposed inference system can assist physicians in identifying the disease and help individuals to perform self-diagnosis on their own cases.
... The implementation of fuzzy inference is based on fuzzy production rules. Fuzzy rules are closest to logical models, but, very importantly, they adequately reflect the knowledge of experts in a particular subject area [15][16][17]. ...
Fuzzy logic plays a key role in various areas, particularly medicine. One of the major problems faced by developed and developing countries is medical treatment. There has been a great deal of concern in finding an alternative to traditional methods of diagnosis due to imprecision and inaccuracy. The fuzzy logic has made identification and diagnosis process rapid, thus minimizing the intervention time and treatment methods with greater accuracy. In fuzzy logic, type 2 logic systems and type 2 Fuzzy-based methods are preferred since they offer the major advantage of demonstrating a variety of outputs and provide a powerful framework for reasoning. This chapter includes the study of type 2 fuzzy logic systems used for treating particular diseases and their role in the development of the medical diagnostic system. The first aim broadly discusses how fuzzy logic has evolved over the years and its need in different sectors. The second aim is to analyze the applications of type 2 fuzzy logic in various branches and discusses recent developments that have occurred mainly in the medical sector. The third aim is to include type 2 fuzzy systems that are used to treat particular diseases paving the way to find further scope for further developments.KeywordsType-2 fuzzy logicType-2 fuzzy-based methodsDisease diagnosis
Breast cancer (BC) seriously threatens women's health. The establishment of a risk evaluation model cancer is conducive to early screening and prevention of BC. In this article, a new method of the evaluation and the framework of prevention for BC are proposed by synthesizing interval type-2 fuzzy sets (IT2 FSs), two-level fuzzy comprehensive evaluation, and parallel control (Artificial societies, Computational experiments, and Parallel execution, ACP). At the same time, 12 risk factors were selected as indicators to evaluate the risk level of BC, and then, according to the evaluation results, the correspondingly prevention strategies and intervention measures for different risk levels were discussed. Then, the prevention strategies are analyzed and selected in the computational experiment module of the artificial system, and the patient indexes are monitored in real time by the parallel mechanism between the actual and the artificial system, to feedback, adjust, and optimize the prevention scheme in time. This parallel BC prevention process can achieve dynamic closed-loop control effects. A new effective way for BC prevention was presented, which is of great significance for reducing its incidence rate and developing new medical means.
Neonatal pathological jaundice (NPJ) is easy to cause bilirubin encephalopathy, which has high mortality and sequelae rate. Therefore, accurate risk evaluation can help clinicians take appropriate measures to timely intervene in neonatal jaundice level and avoid complications. In this article, five indexes are extracted as the factor set for the risk evaluation of NPJ, and the diagnostic criteria are determined. Then, five index sets are described by interval type-2 fuzzy sets, and the corresponding membership functions and membership function figures are provided. The feasibility of interval type-2 fuzzy comprehensive evaluation in risk evaluation of NPJ is demonstrated through example, and the fuzzy rule bases of prevention and treatment for NPJ are constructed according to the results of risk evaluation. Finally, this article demonstrates that the proposed risk evaluation and treatment process of NPJ is actually a dynamic closed-loop control process, which is consistent with the clinical treatment process. This article provides a new solution for the aided diagnosis and decision-making treatment of NPJ, which is of great significance in reducing neonatal mortality and alleviating the pressure of medical staff.
For diabetes mellitus (DM), the technology of blood glucose monitoring provides detection information for patients and helps sufferers to ameliorate bad states. Management rules and intervention measures, which are consistent with blood glucose situation, help to control blood glucose stability, establish a healthy lifestyle, and prevent the occurrence of DM complications. Formulating effective rules and measures is the key to the blood sugar management. What is more, the analysis of blood glucose situation is beneficial to the formulation of management rules and intervention measures. In this paper, interval type-2 fuzzy sets (IT2 FSs) and fuzzy comprehension evaluation are applied in the analysis of blood sugar situation. Moreover, dynamic fuzzy rules are built to provide the corresponding blood glucose management rules. Linguistic dynamic systems (LDS) are used to describe and analyze the evolution process of blood glucose situation. Finally, the analysis results indicate the method is applicable and valuable for actual management.
