Diagnosis of Liver Diseases using Machine Learning

Abstract

Computer aided diagnosis on medical domain such as
breast cancer detection, brain tumor, liver disease, etc
plays a major rile to go for smart hospitality. Liver
diseases are responsible for more than 2.4% of annual
deaths in India. Early detection of liver conditions is
challenging due to the mildness of initial symptoms,
which often become noticeable only at advanced stages.
This paper seeks to enhance liver disease diagnosis by
investigating two methods for identifying patient
parameters. Due to liver diseases many peoples across
the world lost their lives and its death rate can be
reduced only by diagnosing disease on time but the
main problem is LIVER will not show any symptoms
for earlier damage. So in this paper is applying two
methods to predict liver disease. Method1) in this
method author is using INDIAN LIVER dataset to train
various machine learning algorithms such as SVM,
ANN and multilayer perceptron and this trained model
will be applied on new patients TEST data to predict
liver is normal or not but student ask us to implement
Logistic Regression, Naïve Bayes and then compare its
performance with SVM so we are using student
suggested algorithms. Method2) in this method author
is training ANN and CNN with gene MRNA images
dataset and then training with CNN and ANN to predict
whether liver disease inheriting in genes from
ancestors. Student also asking to used liver images and
then train with CNN and ANN; we are using LIVER
ULTRA SOUND SCAN IMAGES