AN EFFECTIVE APPROACH FOR EARLY LIVER DISEASE PREDICTION
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Date
2020-12-08
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Abstract
Liver is one of the main organ of our body. It will be functioning normally even when
it is partially damaged, therefore problems with liver patients cannot easily discovered
in an early stage. Patient’s survival rate can be increased by an early identification of
liver problems. Liver disease can be diagnosed by observing the levels of enzymes in
the blood. Many researchers working on this issue and try to find the best algorithm
with using Liver patient dataset which is suitable for predicting disease in early age. In
this research liver patient dataset is investigated for building different classification
models to get better result by comparing with the accuracy of the classifiers for disease
prediction. Real data in this purpose is collected from the hospitals. This paper uses
different classification methods including Bagged Trees, Support vector machine
(SVM), K-Nearest Neighbor (KNN), Fine Tree classification methods. It can give a
good impact on the liver disease diagnosis and it can be beneficial for physicians and
can help to reduce the cost of diagnosis in the medical sector.