A fuzzy expert system design for diagnosis of prostate cancer

Sarıtaş, İsmail; Allahverdi, Novruz; Sert, İbrahim Ünal

doi:10.1145/973620.973677

Cited by 51 publications

(28 citation statements)

References 8 publications

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“…Mailafiya and Isiaka [15] developed an expert system to diagnose and proffer treatment for Hepatitis B. Brasil and colleagues developed a hybrid expert system to provide diagnosis and advisory services for Epilepsy crisis [16]. In another research, Saritas and colleagues [17] developed a fuzzy expert system for the diagnosis and analysis of prostate cancer. In another study, a rule-based expert system was developed for fever diagnosis by Tunmibi and colleagues [18].…”

Section: Review Of Relevant Literaturementioning

confidence: 99%

Ebinformatics: Ebola fuzzy informatics systems on the diagnosis, prediction and recommendation of appropriate treatments for Ebola virus disease (EVD)

Oluwagbemi

Abimbola

2016

Informatics in Medicine Unlocked

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a b s t r a c tEbola Virus Disease (EVD) also known as the Ebola hemorrhagic fever is a very deadly infectious disease to humankind. Therefore, a safer and complementary method of diagnosis is to employ the use of an expert system in order to initiate a platform for pre-clinical treatments, thus acting as a precursor to comprehensive medical diagnosis and treatments. This work presents a design and implementation of informatics software and a knowledge-based expert system for the diagnosis, and provision of recommendations on the appropriate type of recommended treatment to the Ebola Virus Disease (EVD).In this research an Ebola fuzzy informatics system was developed for the purpose of diagnosing and providing useful recommendations to the management of the EVD in West Africa and other affected regions of the world. It also acts as a supplementary resource in providing medical advice to individuals in Ebola -ravaged countries. This aim was achieved through the following objectives: (i) gathering of facts through the conduct of a comprehensive continental survey to determine the knowledge and perception level of the public about factors responsible for the transmission of the Ebola Virus Disease (ii) develop an informatics software based on information collated from health institutions on basic diagnosis of the Ebola Virus Disease-related symptoms (iii) adopting and marrying the knowledge of fuzzy logic and expert systems in developing the informatics software. Necessary requirements were collated from the review of existing expert systems, consultation of journals and articles, and internet sources. Online survey was conducted to determine the level at which individuals are aware of the factors responsible for the transmission of the Ebola Virus Disease (EVD). The expert system developed, was designed to use fuzzy logic as its inference mechanism along with a set of rules. A knowledge base was created to help provide diagnosis on the Ebola Virus Disease (EVD). The Root Sum Square (RSS) was adopted as a fuzzy inference method. The degree of participation of each input parameter was shown using the triangular membership function and the defuzzification technique used is the Center of Gravity (CoG).The resulting software produced a user-friendly desktop-based, Windows-based, application and the tools used were explained in the results section in three (3) separate phases. First, a comprehensive online survey was conducted over a period of about 3-9 months. 100 Participants participated in the survey on the perception and knowledge analysis of different individuals about Ebola Virus Disease (EVD) transmission factors. 31% of the participants didn't know that there is presently no cure for Ebola. 28% believed that there is presently a cure for Ebola. 43% agreed that Ebola is both air-borne and water-borne, while 33% disagreed, 24% do not know. 23% believed that insects and mosquitoes can help in transmitting the Ebola Virus Disease (EVD), while 30% were completely ignorant. We noticed that ignorance was a major limiti...

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Section: Review Of Relevant Literaturementioning

confidence: 99%

Ebinformatics: Ebola fuzzy informatics systems on the diagnosis, prediction and recommendation of appropriate treatments for Ebola virus disease (EVD)

Oluwagbemi

Abimbola

2016

Informatics in Medicine Unlocked

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“…Other examples include a diagnostic decision support system for acute abdominal pain as presented in [34] and a Fuzzy Expert System (FES) design for the diagnosis, analysis and understanding prostate cancer diseases [35]. FES has also been used to design a system which helps to determine future coronary heart disease risk of patients [11].…”

Section: Basics Of Fuzzy Logicmentioning

confidence: 99%

Clinical decision support system for dental treatment

Mago

Bhatia

et al. 2012

Journal of Computational Science

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“…The introduction of Artificial Intelligence in Medicine did not take place until the second half of the past century [5] and it has largely been used in the different areas including the medical applications in recent years [6].…”

Section: Introductionmentioning

confidence: 99%

Type-2 fuzzy rule-based expert system for Ankylosing spondylitis diagnosis

Maftouni

Türkşen

Zarandi

et al. 2015

2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) Held Jointly With 2015 5th World Con

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Ankylosing spondylitis (AS) is a chronic inflammatory disease. The pathogenesis of AS is poorly understood. However its association with human leukycyte antigen (HLA)-B27 is known. AS causes significant pain, disability, and social burden around the world [1]. Early diagnosis and treatment of AS are necessary in order to prevent or reduce all types of costs associated with loss of function. If the diagnosis is missed, however, the quality of patient's life will degrade. Besides, as a positive family history of AS is a strong risk factor for the disease, this negligence will put other family member's in jeopardy.Nowadays, Expert systems play a big role in diagnosis of patients with different diseases. The application of expert system to diagnose diseases started in the 70s with the development of Mycin. Expert systems in medical diagnosis can help in storing more knowledge than before and make it accessible in absence of a specialist and increase distribution of expertise.Our goal in this paper is to design a type-2 fuzzy rule-based expert system for AS diagnosis where the rules are evidencebased. The basic aim of evidence-based practice is to establish a narrow set of criteria for diagnosis based on research studies. System has mainly two parts. Firstly, the suspicion of disease is assessed for the persons identity according to odds ratio studies and patient's family history. Then the modified New York criteria (1984) for exploring sign and symptoms and the HLA-B27 examination result are considered.The system benefits from fuzzy reasoning and can manage the uncertain inputs through fuzzifying them and making use of type-2 fuzzy rules. Moreover, the system is connected to a spreadsheet for storing the patient's input data and system's final diagnosis. This system can be used by a non-rheumatologist in the diagnosis of AS or by a rheumatologist as an assistant.

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A fuzzy expert system design for diagnosis of prostate cancer

Cited by 51 publications

References 8 publications

Ebinformatics: Ebola fuzzy informatics systems on the diagnosis, prediction and recommendation of appropriate treatments for Ebola virus disease (EVD)

Ebinformatics: Ebola fuzzy informatics systems on the diagnosis, prediction and recommendation of appropriate treatments for Ebola virus disease (EVD)

Clinical decision support system for dental treatment

Type-2 fuzzy rule-based expert system for Ankylosing spondylitis diagnosis

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