Liver Disease Prediction Using Ensemble Technique

Liver disease is a very serious illness. It occurs in the body, most commonly in the liver. The liver’s main job is to remove waste produced by the body. It also stores essential vitamins needed by the body so that they are not wasted. The liver also helps digest food. This disease is serious. The first step is to reduce the chance of contracting it. Being able to detect it early saves lives. Around 3.5 percent of people in the world have liver disease. Machine learning models have been employed in various new methods to predict diseases like KNN, random forest, SVM, and logistic regression. Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) are some of the deep learning approaches that are used for solving this problem. There is no doubt that these approaches will greatly extend the lives of the people suffering from this condition and prevent them from having chronic liver diseases (CLD). The use of automated transactions, bar codes on products alongside better data collection systems has led to collection of huge volumes of information. The proposed system uses ensemble methods like random forest, ADA boost, and gradient boost combined to get better accuracy.