Research
FUZZY CLUSTER MEANS EXPERT SYSTEM FOR THE DIAGNOSIS OF TUBERCULOSIS
Tuberculosis (TB) is a global public health problem of enormous dimension. Tuberculosis is usually associated with mycobacterium tuberculosis (the bacterium causing tuberculosis). TB is an infectious disease, transmitted and spread via aerosols (droplets from the mouth and respiratory tract) that are coughed, sneezed, or forcibly expelled from the body to the surrounding air. These droplets, when inhaled by a susceptible host, can infect a new person and, within weeks to months, the disease begins to develop within the infected person. The lungs are the primary site of infection. The disease can spread to almost any other organ such as: kidneys, bladder, bones, spine, liver, spleen and brain. TB symptoms are characterized by low grade fever, coughing, fatigue, and a loss of appetite. Later, hemoptysis (coughing up blood), may occur. The application of Fuzzy Cluster Means (FCM or Fuzzy C-Mean) analysis to the identification of different types of tuberculosis is the focal point of this paper. Application of cluster analysis involves a sequence of methodological and analytical decision steps that enhances the quality and meaning of the clusters produced. The uncertainties often associated with analysis of tuberculosis test data are eliminated by the proposed system.
AN EXPERT SYSTEM FOR THE INTELLIGENT DIAGNOSIS OF HIV/AIDs USING FUZZY CLUSTER MEANS ALGORITHM
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.
