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Comparison of Classifiers in Data Mining?

Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.3, No. 11)

Publication Date:

Authors : ; ;

Page : 102-115

Keywords : Hepatitis; Data Mining; NB TREE; NAÏVE BAYES; SMO; Weka Tool;

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Hepatitis virus infection substantially increases the risk of chronic liver disease and hepatocellular carcinoma in humans and also affects majority of population in all age groups. It is the major challenge for many hospitals and public health care services for diagnosing hepatitis. Accurate diagnosis and exact prediction of the disease on time can save many patients life and there health. Hepatitis viruses are the most common cause of hepatitis in the world but other infections, toxic substances (e.g. alcohol, certain drugs), and autoimmune diseases can also cause hepatitis. Using Data mining which is a effective tool to diagnose hepatitis and to predict result. This paper review the many data mining techniques which diagnosis hepatitis virus. I will compare three algorithms. All algorithms use different mechanism. I will analyze the result of all and will conclude the algorithm which gives the maximum accuracy for detection of hepatitis.

Last modified: 2014-11-13 20:24:25