Prediction and Classification of Fatty Liver Disease Thesis- A Survey
Keywords:
Fatty liver, Machine Learning, Neural Networks, Segmentation, Classification, Feature ExtractionAbstract
Liver illnesses have recently become the disorder with the highest mortality rate in a lot of countries. Consuming alcohol, breathing in toxic gases, eating food that has gone bad, and taking medications has all contributed to an increase in the number of persons diagnosed with liver disease. Research on patient data sets pertaining to the liver is being carried out in attempt to construct classification models that can anticipate liver disorder. This data set was utilised in the implementation of prediction and classification algorithms, which in turn reduced the amount of work that needed to be done by clinicians. In this research, we explore various machine learning techniques that can be used to evaluate a patient's liver condition comprehensively. The term "chronic liver disorder" refers to any condition that affects the liver and lasts for at least six months. As a consequence of this, we shall make use of the percentage of patients who become infected with the disease as both a positive and a negative piece of information. The percentages of liver disease are being discussed in this work with classifiers, and the findings are being presented in the form of a confusion matrix. We presented numerous classification strategies that, when used in conjunction with a data set for training, have the potential to significantly increase classification performance. Then, using a classifier that was learned by machine learning, the values are separated into good and poor categories. This article presents and discusses the various methods that can be used for the forecasting and categorization of fatty liver disease.
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