Prediction Of Liver Disease Using Statistical Machine Learning Methods

Abstract

In India, liver disease ranks as the tenth leading cause of death, causing 2.95 percent of all deaths. According to the World Health Organization, liver disease is one of the main causes of death in India. With approximately 10 lakh new cases being diagnosed annually, it has developed into a significant threat in India. One of the most critical parts of automated disease detection and prediction is data mining. Medical data is analyzed using data mining algorithms and methods. Disorders of the liver have increased dramatically in recent years, and liver disease is now one of the leading causes of death in several nations. The patient datasets are looked at so that classification models that can predict liver disease can be made. The aforementioned study used feature implementation and comparative analysis to increase the accuracy rate for liver patients over the course of three phases.On the existing datasets of liver patients derived from the first process's data sets, the min-max normalization algorithm is applied. In the second stage of liver dataset prediction, PSO feature selection is used to extract a subset (data) of liver patient datasets from all normalized liver patient datasets that only contains significant attributes.