Abstract:
We all know that the liver is one of the most important organs of our body function. Once
upon a time we couldn’t see that a large number of people were suffering from liver
diseases. But in recent years we can see that the number of patient of liver problem is
increasing day by day. So this affected people should go to a medical center for checking.
But in this COVID situation it is risky for going to the medical center. So in this thesis
we are working for the liver affected people by which they don’t need to go outside for
checking the possibility of Liver disease of them. We took some data based on some
basic attributes which related to liver diseases and make a classifier model for predicting
the possibility of liver diseases. Then we gave some data on that classifier model. The
data carries both the liver affected people and non-affected people. This data is used for
training the machine about to identify the affected people and non-affected people easily.
Then we run some algorithm like KNN, Naïve Bayes, Decision Tree and SVM on that
model and generate some results based on these algorithms. We did the evaluation into
two different approaches. Firstly we generated the complete result with all the attributes
from the selected data. After that we selected some attribute from that data and run the
classifier algorithm on that. It generated some different result which gave us better
accuracy from all attribute based result. In this approach people will know their liver
diseases possibility easily at home which will save time and hassle as well
