International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 06 | June-2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 1058 Recognition and Cure Time Prediction of Swine Flu, Dengue and Chicken Pox using Fuzzy Logic Ravinkal Kaur 1 , Virat Rehani 2 1 M.tech Student, Dept. of CSE, CT Institute of Technology & Research, Jalandhar, India 2 Assistant Professor, Dept. of CSE, CT Institute of Management & Information Technology, Jalandhar, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Healthcare is the maintenance of health via the diagnosis, treatment, prevention of disease and illness. With diseases like swine flu and dengue fever, chicken pox, on the rise, which have symptoms, are so closely associated that it sometimes become practically Herculean task to differentiate between the above-scribed diseases based on symptoms. Thus, it becomes inevitable to design such a system that would closely monitor the symptoms and infer the disease based on FIS (fuzzy inference system). We do this by assigning different coefficients to each symptom of a disease and to predict and quantify the severity impact of the recognized disease. For predicting the cure time of a disease, based on the symptoms. Perdition of cure time is clinically based on hypothetic studies and to estimate the cure time of a disease based on the symptoms. We also infer the current medical condition of a user relative to people who have suffered from the same disease. Key Words: Medical, Fuzzy Logic, Fuzzy inference model, Mamdani Model, GUI, De-fuzzification. 1. INTRODUCTION Fuzzy logic was advanced in 1965 by Dr. Lotfi Zadeh a professor at the University of California, Berkley. One kind of uncertainty is fuzziness that is no sharp transition from complete membership to non-membership. In human reasoning much of the logic is not based on two values, it is not even multi-valued but fuzzy truth. In conventional logic everything is considered true or false, black or white but nothing in between. The Fuzzy logic idea is similar to the human being’s feeling and inference process which is a point-to-point control or range-to-range control. The output of a fuzzy controller is borrowed from fuzzifications of both inputs and outputs using the identify membership functions. A crisp input will be transformed to the different members of the identity membership functions established on its value. From this point of view, the output of a fuzzy logic controller is established on its memberships, which can be tested as a range of inputs. The idea of fuzzy logic was advanced by Dr. Lotfi Zadeh of the University of California at Berkeley in 1965. This development was not well recognized until Dr. E. H. Mastrategymdani who is a professor at London University, related the fuzzy logic in an applied application to control an automatic steam engine in, which is approximately ten years after the fuzzy theory was created. To control cement kilns in 1976, Blue Circle Cement and SIRA in Denmark established an industrial application. That system began to operation in 1982. Fuzzier implementations have been since the 1980s, along with those utilizations in industrial manufacturing, automobile production, banks, hospitals and academic education. The main aim is to construct a control system that will provide good transient and steady state reply of the system. Fuzzy logic develops into a standard technology and is also applied in data and sensor signal analysis. Fuzzy logic has verified to be a powerful tool for decision-making systems, such as expert systems and pattern classification systems. Dr. Zadeh was working on the difficulty of computer understanding of natural language. Formation of the fuzzy knowledge base in MATLAB can be done using a tool Fuzzy Logic Toolbox [2]. The Toolbox is a suite of software applications that make up the environment Matlab. It allows you to create fuzzy inference system and fuzzy classification in the environmental MATLAB, i.e., functionally driven to the formation of versatile classification for data systems. The base element in the Collection is the FIS-structure, i.e. the Fuzzy Inference System. FIS-structure contains the necessary functional blocks for implementation of fuzzy inference [3]. The Medical Diagnosis System takes input in the form of symptoms and gives output in the form of a particular disease. The fuzzy rules used in the system are based on expert knowledge. 2. BACKGROUND AND RELATED WORKS