An Effective Methodology to Forecast the Progression of Liver Disease and Its Stages Using Ensemble Technique

Liver disease is one of the deadliest illnesses in the world. It takes place in the human body, particularly in the liver. The liver filters all of the blood present in the body and detoxicates harmful substances such as alcohol and drugs. Finding the origin and severity of liver disease is crucial for an effective treatment. The risk of liver disease can be predicted using various machine learning algorithms. We created a system that requests users submit the specifics of their blood test report based on the precise model. The system then uses the most precise model that has been trained to forecast whether or not a person is at risk for liver disease. Data preparation, data pre-processing, feature selection, classification, and building a model is the work flow which we followed. Indian_liver_patients and cirrhosis datasets are used. Mean, median, and standard deviation calculations are performed to enhance the input text data. The datasets consist of numerical and categorical data which is further pre-processed to remove the categorical and noise present within the dataset. Some unwanted features are eliminated using feature selection. With the aid of machine learning classification methods like Random Forest, Support Vector Machine, K-NN and Naive Bayes, liver illness can be detected early. The proposed work is an ensemble technique to predict the liver disease and its stages with higher accuracy when compared with individual classification techniques. Here we propose the combination of Random Forest, AdaBoost and GradientBoost classifiers. Concurrently, we use RandomizedSearchCV and GridSearchCV which are two effective ways that tune the parameters to increase the model generalizability. The performance metrics we considered are accuracy, precision, recall and F1-score.