Medical Diagnosis for Liver Cancer Using Classification

Reetu Sabharwal, Narender Kumar


One of the applications of data mining is medical diagnosis which is mostly used in research area. Medical diagnosis is the field where many researchers are concentrating. To reduce the diagnosis time and improve the diagnosis accuracy, it has become an important issue. In medical, Liver Cancer is one of the most prevalent and deadly cancers in human beings. Liver cancer is difficult to be diagnosed at an early stage due to the risk factors. Therefore, new metrologies for early Liver Cancer are needed to determine the condition of the Liver Cancer. Various Data classification techniques or algorithms are used to solve this issue. Some classification techniques or algorithms are Decision tree, C4.5, Bayesian networks, Conjunctive Rule Learner, SOM, K-NN, Neural networks etc. Recent research studies on liver diagnosis indicated difference in classification accuracy of various classifiers with different data sets. So, in this paper we used the Three Liver Cancer Dataset to classify the datasets. First dataset is the live dataset which is taken from Pt. B. D Sharma Postgraduate Institute of Medical Sciences Rohtak and other two dataset is taken from the UCI machine data repository in which one is Indian liver patient’s dataset and other is BUPA liver disorder dataset.


Liver Cancer, Decision Tree, J48, SOM, Neural Network, Conjunctive Rule Learner, Bayesian Networks.

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