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\title{An Expert System for the Intelligent Diagnosis of Hiv Using Fuzzy Cluster Means Algorithm}
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             \author[1]{Imianvan  A.A.}

             \affil[1]{  University of Benin, Edo State}

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\date{\small \em Received: 23 February 2011 Accepted: 22 March 2011 Published: 2 April 2011}

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\begin{abstract}
        


Human Immunodeficiency Virus (HIV) is a retrovirus that causes Acquired Immune Deficiency syndrome (AIDS) by infecting helper T cells or Lymphocyte of the immune system. HIV is transmitted primarily by exposure to contaminated body fluids, especially blood and semen. Other means of transmission of HIV include sharing contaminated sharp objects and blood transfusion. HIV symptoms can include: headache, chronic cough, diarrhea, swollen glands, lack of energy, loss of appetite, weight loss, frequent fevers, frequent yeast infections, skin rashes, pelvic/abdominal cramps, sores on certain parts of your body and short-term memory loss. The focal point of this paper is to describe and illustrate the application of fuzzy cluster means system to the diagnosis of HIV. It involves a sequence of methodological and analytical decision steps that enhances the quality and meaning of the clusters produced. The proposed system eliminates theuncertainties often associated with analysis of HIV test data.

\end{abstract}


\keywords{Fuzzylogic, Clustering, FuzzyC-Means, HIV.}

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\let\tabcellsep& 	 	 		 
\section[{INTRODUCTION}]{INTRODUCTION}\par
uman immunodeficiency virus (HIV) is a retrovirus (a virus whose genetic information is contained in Ribonucleic acid instead Deoxyribonucleic acid) that causes Acquired Immune Deficiency Syndrome (AIDS) by infecting helper T cells or Lymphocyte (cells that defense the body at foreign bodies) of the immune system.\par
Antigen are substances that stimuli the production of antibody. A serotype or serovar is a group of bacteria that share a characteristic set of antigen. The most common serotype or serovar, HIV-1, is distributed worldwide, while HIV-2 is primarily confined to West Africa \hyperref[b9]{(Healthline, 2011)}. AIDS is a severe immunological disorder caused by the retrovirus HIV, resulting in a defect in cell-mediated immune response that is manifested by increased susceptibility to opportunistic infections and to certain rare cancers, especially Kaposi's sarcoma  {\ref (Healthline and MedicineNet, 2011)}. It is transmitted primarily by exposure to contaminated body fluids, especially blood and semen. Other means include sharing contaminated sharp objects and blood transfusion. Everybody who has AIDS also has HIV, but not everybody with HIV is classified by the United States (U.S.) government as having AIDS. The U.S. government uses CD4 cell counts (part of the immune system) to make this distinction \hyperref[b9]{(Healthline, 2011)}.\par
The earliest known case of HIV-1 came from a human blood sample collected in 1959 from a man in Kinshasa, Democratic Republic of Congo  {\ref (healthline, 2011)}. The method by which he became infected is not known; however, genetic analysis of his blood sample suggested that HIV-1 might have stemmed from a single virus in the late 1940s or early 1950s. HIV has existed in the United States since the mid to late 1970s. During 1979 to 1981, rare types of pneumonia, cancer, and other illnesses were reported by physicians in Los Angeles and New York among a number of male patients who had sex with other men. Los Angeles and New York among a number of male patients who had sex with other men. Since it is rare to find these diseases in people with a healthy immune system, public health representatives became concerned that a new virus was emerging \hyperref[b9]{(Healthline, 2011)}.\par
In 1982, the term AIDS was introduced to describe the occurrences of opportunistic infections, Kaposi sarcoma, and pneumonia (Pneumocystis carinii ) in previously healthy persons and formal tracking of these cases in the United States began that year. The virus that causes AIDS was discovered in 1983 and named human or helper T-cell (lymphotropic) virus-type III/ lymphadenopathy associated virus (HTLV-III/LAV) by an international scientific committee who later changed it to HIV \hyperref[b9]{(Healthline, 2011 and}\hyperref[b11]{MedicineNet, 2011)}. Many theories as to the origins of HIV and how it appeared in the human population have been suggested. The majority of scientists believed that HIV originated in other primates and was somehow transmitted to man. In 1999, an international group reported the discovery of the origins of HIV-1, the predominant strain of HIV in the developed world \hyperref[b9]{(Healthline, 2011)}. A subspecies of chimpanzees native to west equatorial Africa were identified as the original source of the virus. The researchers believe that HIV-1 was introduced into the human population when hunters became exposed to infected blood \hyperref[b9]{(Healthline, 2011;} {\ref MedicineNet, 2011 and WrongDiagnosis)}.\par
Most scientists believe that HIV causes AIDS by directly inducing the death of CD4+ T cells (helper T person's immune response is disrupted during HIV infection, impairing a person's ability to fight other infections. The HIV-mediated destruction of the lymph nodes and related immunologic organs also plays a major role in causing the immunosuppressant seen in persons with AIDS \hyperref[b9]{(Healthline, 2011)}.\par
In the absence of antiretroviral therapy, the median time from HIV infection to the development of AIDS-related symptoms has been approximately 10 to 12 years \hyperref[b9]{(Healthline, 2011 and}\hyperref[b12]{WrongDiagnosis, 2011)}. A wide variation in disease progression, however, has been noted. Approximately 10 percent of HIV-infected persons have progressed to AIDS within the first two to three years after infection, whereas up to 5 percent of persons have stable CD4+ Tcell counts and no symptoms even after 12 or more years \hyperref[b9]{(Healthline, 2011)}. Factors such as age or genetic differences among persons with HIV, the level of virulence of an individual strain of virus, and co-infection with other microbes may influence the rate and severity of disease progression. Drugs that fight the infections associated with AIDS have improved and prolonged the lives of HIV-infected persons by preventing or treating conditions such as Journal of Infectious Diseases. This approach is known formally as short-cycle structured intermittent antiretroviral therapy (SIT) or colloquially as the "7-7" approach \hyperref[b9]{(Healthline, 2011)}.\par
HIV symptoms can include: headache, chronic cough, diarrhea, swollen glands, lack of energy, loss of appetite, weight loss, frequent fevers, frequent yeast infections, skin rashes, pelvic/abdominal cramps, sores on certain parts of your body and short-term memory loss \hyperref[b11]{(MedicineNet, 2011)}.\par
Existing methods of medical diagnosis employed by physicians for the analysis of HIV infection uses manual methods characterized by the inability to handle uncertain or vague data existing between intervals. More so, those systems are not self-learning or adaptive in nature. This paper has chosen to solve these problems by employing the rich facilities of fuzzy cluster means. The proposed system which is self-learning and adaptive, is a time-capsule (a cache of information) to be preserved for ages to medical engineers for the diagnosis and analysis of HIV infection. 
\section[{II.}]{II.} 
\section[{LITERATURE REVIEW}]{LITERATURE REVIEW}\par
Cluster analysis is a statistical techniques used to classify objects into coherent categories based on a set of measurement, indicators or variables. A common use of cluster analysis in medicine is to categorize patients into subgroups or diagnostic categories based upon patterns of clinical signs and symptoms, in this case HIV infection \hyperref[b3]{(Brian et al., 2001)}. Two-way clustering techniques are frequently used to organize genes into groups or clusters with similar levels of expression across relevant subgroup of patient's tissues, sample or cell lines \hyperref[b7]{(Eisen et al., 1998)} In practice, a cluster analysis is the product of a series of analytical decisions. The analytical decisions made at each point in the series can significantly affect subsequent decisions, as well as the overall result of a cluster analysis \hyperref[b8]{(Everitt et al., 2003)}. This series of analytic decisions typically involves choices about what objects to cluster, unit of measurement to use for the variables, proximity measure and criteria for determining the number and quality of clusters within the data.\par
Likert scale is the most popular psychological measurement schemes that depend on human judgment. This scaling scheme assures that the human observer is good at quantitative observation and assignment of number or objects to reflect degrees of traits or statement being measured \hyperref[b4]{(Cartwrigh, 2003 and}\hyperref[b5]{Chow, 2002)}. In this scoring scheme, subjects are asked to choose exactly one alternative that describe their substance \hyperref[b15]{(Yuan, 2008}). However, this scheme disregard with human thinking as multi-valued, transitional and analogue, but rather clear-cut (precise) and digital.\par
The invention and application of Fuzzy Cluster Means (FCM) algorithm in pattern recognition allows entities (objects) to belong to many clusters or categories with different degrees of membership (Yiouyang et al.; 2007). In this paper a framework for partitioning, which proposes a model of how data are generated from a cluster structure is presented. The Fuzzy Logic and Neural networks of personnel performance within organizations has been studied with a view of evaluating them for productivity and promotion \hyperref[b0]{(Akinyokun and Uzoka, 2004)}. The application of Fuzzy C-means (FCM) algorithm to medical diagnostic expert systems is presented in \hyperref[b1]{(Albayrak and}\hyperref[b1]{Amasyali, 2003 and}\hyperref[b2]{Berk et al., 2000)}. This algorithm is used in assigning patients to different cluster of disease. The application of fuzzy C-means in clustering has been demonstrated in \hyperref[b13]{(Yang and Wang, 2001}; De Fazio and Galeazzi, 2004 and Jantzen , 1998). In this paper, fuzzy C-means algorithm is used to assign patients with HIV conditions to clusters of HIV. 
\section[{Overview of Fuzzy C-Means Clustering (FCM)}]{Overview of Fuzzy C-Means Clustering (FCM)}\par
The FCM algorithm is one of the most widely used fuzzy clustering algorithms. The FCM algorithm attempts to partition a finite collection of elements X=\{X 1 , X 2 ,...,X n \} into a collection of c fuzzy clusters with respect to some given criterion. Given a finite set of data, the algorithm returns a list of c cluster centers V, such that V =V i, i=1, 2,..., c and a partition matrix U such that U = U ij , i =1,..., c , j =1,..., n 
\section[{2011}]{2011}\par
where U ij is a numerical value in [0, 1] that tells the degree to which the element X j belongs to the i-th cluster.\par
The fuzzy logic linguistic description of the typical FCM algorithm is presented in Figure  {\ref 1} Start\par
Step 1: Select the number of clusters c (2?c?n), exponential weight ? (1<?<?), initial partition matrix U 0 , and the termination criterion ?. Also, set the iteration index i to 0.\par
Step 2: Calculate the fuzzy cluster centers \{V i 1 | i=1, 2,..., c\} by using U 1 .\par
Step 3: Calculate the new partition matrix U 1+1 by using \{V i 1 | i=1, 2,..., c\}.\par
Step  
\section[{METHODOLOGY}]{METHODOLOGY}\par
The process for the medical diagnosis of HIV starts when an individual consults a physician (doctor) and presents a set of complaints (symptoms). The physician then requests further information from the patient or from others close to him who knows about the patient's symptoms in severe cases. Data collected include patient's previous state of health, living condition and other medical conditions. A physical examination of the patient condition is conducted and in most cases, a medical observation along with medical test(s) is carried out on the patient prior to medical treatment.\par
From the symptoms presented by the patient, the physician narrows down the possibilities of the illness that corresponds to the apparent symptoms and make a list of the conditions that could account for what is wrong with the patient. The physician then conducts a physical examination of the patient, studies his or her medical records and ask further questions, as he goes in an effort to rule out as many of the potential conditions as possible. When the list has been narrowed down to a single condition, it is called differential diagnosis and provides the basis for a hypothesis of what is ailing the patient. Until the physician is certain of the condition present; further medical test are performed or schedule such as medical imaging, scan, X-rays in part to conform or disprove the diagnosis or to update the patient medical history. Other Physicians, specialist and expert in the field may be consulted (sought) for further advices.\par
Despite all these complexities, most patient consultations are relatively brief because many diseases are obvious or the physician's experience may enable him to recognize the condition quickly. Upon the completion of the diagnosis by the physician, a treatment plan is proposed, which includes therapy and follow-up (further meeting and test to monitor the ailment and progress of the treatment if needed). Review of diagnosis may be conducted again if there is failure of the patient to respond to treatment that would normally work. The procedure of diagnosing a patient suffering from HIV is synonymous to the general approach to medical diagnosis. The physician may carry out a precise diagnosis, which requires a complete evaluation to determine whether the patient is having HIV. The examining physician accounts for possibilities of having HIV through interview, physical examination and laboratory test. Many primary health care physicians use screening tools for HIV evaluation.\par
A diagnostic evaluation of HIV may include a complete history of the following: a.\par
When did the symptoms start? b.\par
How long have the symptoms lasted? c.\par
How severe are the symptoms? d.\par
Have the symptoms occurred before, and if so, were they treated and what treatment was received?\par
IV. 
\section[{RESULTS AND DISCUSSION}]{RESULTS AND DISCUSSION}\par
To design the FCM Knowledge Base System for diagnosis of HIV, we design a system which consists of a set of parameters needed for diagnosis (here, we are using 13 basic and major parameters) presented in Table  {\ref 1}.\par
Table  {\ref 1} : Symptoms of HIV Figure  {\ref 2} presents the model of the FCM system for the diagnosis of HIV. It comprises of knowledge base system, fuzzy c-means inference engine and decision support system. The knowledge base system holds the symptoms for HIV. The values of the parameters are vague and imprecise hence the adoption of fuzzy logic as a means of analyzing these information. Those parameters therefore constitute the fuzzy parameter of the knowledge base. The fuzzy set of parameters is represented by 'P' which is defined as P= P 1 , P 2 , ?,P n Where P i represents the jth parameter and n is the total number of parameter (in this case n=13). The set of linguistic values which is modelled as a linker scale denoted by 'L' is given as L = (Low, Average and High).\par
Clustering of the data is achieved using the typical FCM algorithm presented in Figure  {\ref 2}. Neural networks provide the structure for the parameters which serves as a platform for the inference engine. The inference engine consists of reasoning algorithms driven by production rules. These production rules are evaluated by using the forward chaining approach of reasoning. The inference mechanism is fuzzy logic driven. The cognitive filter of the decision support engine takes as input the output report of the inference engine and applies the objective rules to rank the individual on the presence or absence of HIV infection. The emotional filter takes as input the output report of the cognitive filter and applies the subjective rules in the domain of HIV studies in order to rank individuals on the extent of the HIV infection.   {\ref 2}) with 13 parameters and 3 clusters which shows the degree of membership of each parameter of HIV is represented using the graph in Figure \hyperref[fig_1]{3}.\par
From Figure \hyperref[fig_1]{3}, it is shown that there are no unitary (crisp) coefficients, indicating that each data point belongs to more than one cluster. For example the parameter " Frequent yeast infection" has its fuzzy set membership function as \{0.1/C 1 + 0.8/C 2 + 0.1/C 3 \} where C 1 , C 2 and C 3 are clusters and in this study represents "Might be HIV infected", "HIV infected" and "Not HIV infected". This represents the degree of membership in terms of percentage as 10\%, 80\% and 10\% match with "Might be HIV infected", "HIV infected" and "Not HIV infected" respectively.\par
Finally, Table  {\ref 2} presents membership grades of parameters in all clusters whereas the degree of membership of the clusters is presented in Figure \hyperref[fig_1]{3}    
\section[{CONCLUSION}]{CONCLUSION} 
\section[{HIV Symptom code}]{HIV Symptom code}\par
The need to design a system that would assist doctors in medical diagnosis has become imperative and hence cannot be over emphasized. This paper present a diagnostic fuzzy cluster means system to help in diagnosis of HIV using a set of symptoms and demonstrates the practical application of ICT (Information and Communication Technology) in the domain of diagnostic pattern appraisal by determining the extent of membership of individual symptoms. This advanced system which uses a set of clustered data set is more precise than the traditional system. The classification, verification and matching of symptoms to the three groups of clusters was necessary especially in some complex scenarios. The fuzzy-cluster means system proposed and tested in this paper appears to be\begin{figure}[htbp]
\noindent\textbf{41}\includegraphics[]{image-2.png}
\caption{\label{fig_0}4 :Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 3 :}\end{figure}
  \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.37119266055045874\textwidth}P{0.03743119266055046\textwidth}P{0.13568807339449543\textwidth}P{0.25811926605504587\textwidth}P{0.012477064220183487\textwidth}P{0.03509174311926606\textwidth}}
\tabcellsep Symptom.\tabcellsep \multicolumn{2}{l}{Symptoms of HIV (Human}\tabcellsep \\
\tabcellsep Codes\tabcellsep \multicolumn{2}{l}{Immunodeficiency Virus)}\tabcellsep \\
\tabcellsep P01\tabcellsep \tabcellsep Headache\tabcellsep \\
\tabcellsep P02\tabcellsep \tabcellsep Chronic cough\tabcellsep \\
\multicolumn{3}{l}{P03 Fuzzy C Means, Physiological features of}\tabcellsep Diarrhea\tabcellsep \\
\multicolumn{3}{l}{P04 HIV: Headache, Chronic cough, Diarrhea, Swollen gland, lack of energy, loss of}\tabcellsep Swollen glands\tabcellsep \\
\multicolumn{3}{l}{P05 appetite, weight loss, frequent fever, frequent}\tabcellsep Lack of energy\tabcellsep \\
\multicolumn{3}{l}{P06 yeast infection, skin rashes, Pelvic/ abdominal cramps, sores on certain parts of}\tabcellsep Loss of appetite\tabcellsep \\
\tabcellsep P07\tabcellsep \tabcellsep Weight loss\tabcellsep \tabcellsep Fuzzy C-Means\\
\tabcellsep P08\tabcellsep \tabcellsep Frequent fever\tabcellsep \tabcellsep Inference Engine\\
Unstructured\tabcellsep P09\tabcellsep \multicolumn{2}{l}{Frequ ent yeast infection Structured}\tabcellsep \\
Information\tabcellsep P10\tabcellsep information\tabcellsep Skin rashes\tabcellsep \multicolumn{2}{l}{DECISION SUPPORT}\\
\tabcellsep P11\tabcellsep \multicolumn{2}{l}{Pelvic/ abdominal cramps,}\tabcellsep \\
\tabcellsep P12\tabcellsep \multicolumn{3}{l}{Sores on certain parts of your body}\tabcellsep Emotional Filter\\
\multicolumn{2}{l}{P13 Knowledge-base}\tabcellsep \multicolumn{2}{l}{Short-term memory loss}\tabcellsep \\
\multicolumn{2}{l}{Database Model}\tabcellsep \tabcellsep \multicolumn{3}{l}{The expert system is developed in an Cognitive}\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{environment characterized by Microsoft XP Professional Filter}\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{operating system, Microsoft Access Database}\\
\multicolumn{2}{l}{Neural Network}\tabcellsep \tabcellsep \multicolumn{3}{l}{Management System, Visual BASIC Application}\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{Language and Microsoft Excel.}\\
\tabcellsep Fuzzy Logic\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Fuzzy C -Means}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Clustering A typical FCM membership grade table (}\tabcellsep \tabcellsep \\
\multicolumn{6}{l}{Figure 2 : Architecture of FCM Knowledge Base System for diagnosis/analysis of HIV}\end{longtable} \par
 
\caption{\label{tab_0}Table}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.07769652650822668\textwidth}P{0.34808043875685557\textwidth}P{0.1693784277879342\textwidth}P{0.12431444241316271\textwidth}P{0.13053016453382085\textwidth}}
\tabcellsep \tabcellsep \multicolumn{3}{l}{DEGREE OF MEMBERSHIP}\\
CODE\tabcellsep SYMPTOMS\tabcellsep Cluster 1 (C 1 )\tabcellsep Cluster 2 (C 2 )\tabcellsep Cluster 3 (C 3 )\\
P01\tabcellsep Headache\tabcellsep 0.20\tabcellsep 0.30\tabcellsep 0.50\\
P02\tabcellsep Chronic cough\tabcellsep 0.10\tabcellsep 0.30\tabcellsep 0.60\\
P03\tabcellsep Diarrhea\tabcellsep 0.25\tabcellsep 0.60\tabcellsep 0.15\\
P04\tabcellsep Swollen gland\tabcellsep 0.25\tabcellsep 0.50\tabcellsep 0.25\\
P05\tabcellsep Lack of energy\tabcellsep 0.22\tabcellsep 0.70\tabcellsep 0.08\\
P06\tabcellsep Loss of appetite\tabcellsep 0.20\tabcellsep 0.80\tabcellsep 0.00\\
P07\tabcellsep Weight loss\tabcellsep 0.27\tabcellsep 0.53\tabcellsep 0.20\\
P08\tabcellsep Frequent fever\tabcellsep 0.30\tabcellsep 0.65\tabcellsep 0.05\\
P09\tabcellsep F requent yeast infection\tabcellsep 0.10\tabcellsep 0.80\tabcellsep 0.10\\
P10\tabcellsep Skin rashes\tabcellsep 0.56\tabcellsep 0.44\tabcellsep 0.00\\
P11\tabcellsep Pelvic/ abdominal cramps,\tabcellsep 0.80\tabcellsep 0.15\tabcellsep 0.05\\
P12\tabcellsep Sores on certain parts of your body\tabcellsep 0.50\tabcellsep 0.37\tabcellsep 0.13\\
P13\tabcellsep Short -term memory loss\tabcellsep 0.70\tabcellsep 0.15\tabcellsep 0.15\\
\multicolumn{2}{l}{RESULTS}\tabcellsep Might be HIV Infected\tabcellsep HIV Infected\tabcellsep Not HIV Infected\end{longtable} \par
 
\caption{\label{tab_1}}\end{figure}
 			\footnote{© 2011 Global Journals Inc. (US)Global Journal of Computer Science and TechnologyVolume XI Issue XII Version I July normal function and by triggering other events that weaken a person's immune function. For example, the network of signaling molecules that normally regulates a} 			\footnote{© 2011 Global Journals Inc. (US)} 			\footnote{JulyAn Expert System for the Intelligent Diagnosis of Hiv Using Fuzzy Cluster Means Algorithm} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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