# Journal Of Computing, Volume 2, Issue 6, June 2010, Issn 2151-9617 Https://Sites.Google.Com/Site/Journalofcomputing/ Www.Journalofcomputing.Org 66 Human Disease Diagnosis Using A Fuzzy Expert System Mir Anamul Hasan, Khaja Md. Sher-E-Alam And Ahsan Raja Chowdhury

Abstract—Human disease diagnosis is a complicated process and requires high level of expertise. Any attempt of developing a web-based expert system dealing with human disease diagnosis has to overcome various difficulties. This paper describes a project work aiming to develop a web-based fuzzy expert system for diagnosing human diseases. Now a days fuzzy systems are being used successfully in an increasing number of application areas; they use linguistic rules to describe systems. This research project focuses on the research and development of a web-based clinical tool designed to improve the quality of the exchange of health information between health care professionals and patients. Practitioners can also use this web-based tool to corroborate diagnosis. The proposed system is experimented on various scenarios in order to evaluate it's performance. In all the cases, proposed system exhibits satisfactory results. 

Index Terms— Expert System, Fuzzy Expert System, Online Diagnosis System, Human Disease Diagnosis, Knowledge based System, knowledge based Fuzzy Expert System. 

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## 1 Introduction

O
NLINE diagnosis is becoming popular day by day. 

In today's world people are so busy, that they hardly have enough time to visit a doctor. So they can use the service of this online diagnosis system residing home or office and have an idea about the disease. After that they can consult the specialist doctor if it is necessary or serious. This research presents a novel method for online diagnosis. In this method, based on the selection of the problem area/ problem, the expert system gives some symptom from which the user needs to select symptoms. Based on the selection of symptoms, the user is again asked some questions. According to the answer selection, the fuzzy expert system diagnosis diseases based on it's knowledge, add catalyst factor (if any), do ranking and gives the result in fuzzy form. As fuzzy expert system deals with uncertainty and vague terms, it is generously accepted in different sphere of life [1]. 

Rest of the paper is organized as follows- Section 2 highlights the background study. Proposed System is presented in Section 3 and evaluation of the proposed system is shown in Section 4. Chapter 5 concludes the paper. 

tem is developed for diagnosis of diseases based on the knowledge given by doctors in the system. 

 A computer Program Capable of performing at a human-expert level in a narrow problem domain area is called an expert system [2]. Management of uncertainty is an intrinsically important issue in the design of expert systems because much of the information in the knowledge base of a typical expert system is imprecise, incomplete or not totally reliable [3]. 

 An expert system that uses fuzzy logic instead of Boolean logic is known as Fuzzy expert system. 

 A fuzzy expert system is a collection of fuzzy rules and membership functions that are used to reason about data. Using fuzzy expert system expert knowledge can be represented that use vague and ambiguous terms in computer. 

 Fuzzy logic is a set of mathematical principles for knowledge representation based on degrees of membership rather than the crisp membership of classical binary logic. Unlike two-valued Boolean logic, fuzzy logic is multi valued. Fuzzy logic is a logic that describes fuzziness. As fuzzy logic attempts to model human's sense of words, decision making and common sense, it is leading to more human intelligent machines [4]. 

 Fuzzy logic was introduced in the 1930 by Jan Lukasiewicz; a Polish Philosopher (extended the truth values between 0 to 1). Later, 1937 Max Black defines first sample fuzzy set. In 1965, Lotfi Zadeh rediscovered fuzziness, identified and explored it [2]. 

 The National Research Council, Canada has developed an online diagnosis system. Initially, they used acute leukaemia disease data to illustrate the efficacy of the diagnostic tool. More recently, they developed a new fuzzy classification method called PROAFTN [5] to assist medical diagnosis. Results obtained using the PROAFTN 
method on acute leukaemia diagnosis are promising. The challenge herein is to provide secure, remote access, to 2 BACKGROUND STUDY
Knowledge is a theoretical or practical understanding of a subject or a domain. In other words, Knowledge is the sum of what is currently known [2]. Diagnosis system is a system which can diagnose diseases through checking out the symptoms. A knowledge based online diagnosis sys-
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 Mir Anamul Hasan is with the Dept. of Computer Science & *Engineering*
(CSE), University of Dhaka, Dhaka, *Bangladesh.*
 Khaja Md. Sher‐E‐Alam is with the Dept. of CSE., University of Dhaka, Dhaka, *Bangladesh.*
 Ahsan Raja Chowdhury is with the Dept. of CSE, University of *Dhaka,*
Dhaka, *Bangladesh.*