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Designing a Disease Diagnosis System by Using Fluffy Set Theory

 Designing an illness Diagnosis System by Using Fuzzy Set Theory Essay

Proceedings with the 5 Hard anodized cookware Mathematical Convention, Malaysia 2009

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BUILDING A DISEASE DIAGNOSIS SYSTEM THROUGH THE USE OF FUZZY ARRANGED THEORY Ahmad Mahir Ur., Asaad A. Mahdi and Ali A. Salih Institution of Mathematical Sciences, Teachers of Scientific research and Technology Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, MALAYSIA E-mail: [email protected] my; [email protected] com; [email protected] com Fuzy: Many diseases affecting lots of people every day. Information technology could be accustomed to reduce the mortality rate and waiting time to see the specialist. As scientific decision making innately requires reasoning under doubt, expert devices, fuzzy collection theory and fuzzy reasoning are a highly suitable basis for growing knowledge primarily based systems in medicine to get tasks just like diagnosis of illnesses, the optimal collection of medical treatments, and then for real time monitoring of individual data. Each of our goal is always to develop a methodology using fluffy set theory to assist general practitioner in diagnosing and guessing patients condition from certain " guidelines based on knowledge ”. Medical practitioner other than specialists may not have enough expertise or perhaps experience to handle certain high risk diseases. With this system the patients with high risk factors or symptoms could be short listed to find the specialists for even more treatment. The intuition is based on doctors capacity to make preliminary judgment based upon his examine and experience. In this daily news we designed a questionnaire to gather the data necessary. We chose a random test of 168 patient via clinics and hospitals. The questionnaire depends upon three diverse sets. In your first set is the conditions symptoms set, which contain information concerning symptoms of conditions such as fever, high temperature, headaches, rash, throwing up, etc .. The second set is the diseases established ( chicken pox, Hepatitis B, etc . ), and then the people set ( a sample of 49 patients), the next step was to form the membership functions for each and every symptom, and after that the membership value was evaluated based on the answers to the concerns relative to the actual feature, intended for the start, simply chicken pox and Hepatitis B were considered in the analysis, then the next step will be expanding to feature four additional diseases such as Dengue, Measles, Flu, and Infectious mononucleosis.

Key Words: Fuzzy sets, unclear max-min relations, Membership ideals, Medical prognosis, Expert systems. 1 . Intro

Medical the diagnosis of is the artwork of identifying a person's another status coming from an offered set of findings (Steimann & Adlassnig, 1997). It is a difficulty complicated by many people factors and its particular solution requires literally all a human's abilities including intuition plus the subconscious, patients coming to the doctor showing symptoms and signs and the doctor will conclude which disease these types of phenomena imply. 2 . Medical Knowledge The history of medical diagnosis is actually a history of intensive collaboration among physicians and mathematicians respectively (Seising ainsi que al., 1999), In the 1960s and 1970s different approaches to digital diagnosis came about using Bayes rule, element analysis, and decision research, On the other side artificial intelligence techniques came in to work with, e. g., Dialog(Diagnostic Logic), and Pip(present illness program), which were applications to imitate the medical doctors reasoning in information gathering and analysis using sources in form of networks of symptoms and diagnoses. All of us use the term symptom for most information about people state of health, indicators laboratory test out results, ultrasonic results, and x- beam findings. Depending on this information a physician has to discover a list of analysis possibilities pertaining to the patient, the certain info on relationship that exists between symptoms and symptoms, symptoms and diagnoses, diagnoses and diagnoses plus more complex human relationships of the combination of symptoms and diagnoses into a symptom or perhaps diagnoses, are formalization of what is named medical know-how. In...

Recommendations: 1 . 2 . 3. 4. Klir, G, J. and Folger, T. A., (1988), fuzzy units, uncertainty and information, Prentice Hall, New Jersey, p71-74. Nguyen, H. Capital t., Prasad, D. R., Master, C. T., Walker, Electronic. A (2003), A first course in fluffy and nerve organs control, Chapman& Hall, l 117-119. Radha, R., and Rajagopalan, S i9000. P., (2007), Fuzzy logic Approach intended for Diagnosis of Diabetics, information technology record 6(1): 96-102. Schuerz, Meters., Adlassnig, K-P, Lagor, C., Scheide, N. and Grabner, G. (1998), Definition of fluffy sets representing medical ideas and acquisition of fuzzy interactions between them by semi-automatic methods, medical information system. Seising, R., Schuh, C., and Adlassnig, K-P, (1999), Medical knowledge, unclear sets, and Expert devices. Seising, L, (2004), A brief history of medical diagnosis applying fuzzy relationship (draft newspaper, fuzziness in finland 04(fif04)) Unpublished daily news. Steiman, Farreneheit., and Adlassnig, K-P, fluffy medical diagnosis. Vig, Ur., Handa, In. M., Bali, H. E., Sridher, (2004), Fuzzy Analysis systems to get Coronary Artery Disease. Vol 85

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