Analysis and Prediction of Liver Cirrhosis Using Machine Learning Algorithms

Liver cirrhosis is a serious and progressive liver disease that results in the formation of scar tissue and liver dysfunction. It is one of the key reasons why people die and morbidity worldwide, affecting millions of people. The illness known as cirrhosis of the liver causes the liver's healthy tissue to be replaced by scar tissue, which impairs its ability to function. Liver is a crucial organ which performs various purposes, including filtering toxins from the bloodstream, producing bile for digestion, and regulating glucose levels. When cirrhosis progresses, it can lead to liver failure, which can be life-threatening. The cost and complexity of this disease's diagnosis are enormous. This project is to compare the effectiveness of several ML techniques to lower the chronic liver disease through various models. We used numerous algorithms in this paper for example LogisticRegression, KNeighbours, SVM, Naïve Bayes, RandomForest and many more.The analysis result shows the Random Forest achieved the highest accuracy.