Liver disease prediction using ensemble model

Abstract

Despite the fact that medical science has progressed to the point where it is capable of identifying and prescribing the exact cure for known diseases, traditional medicinal practices are outdated and slow and it can take so long to identify a disease that it becomes fatal to patients who seek immediate attention. Because the liver plays such a crucial role in eliminating toxins from our bodies, this research focuses mostly on the prediction of liver disorders such as jaundice and various forms of hepatitis. As a result, early detection and treatment of liver illnesses are critical for successful diagnosis and recovery. For the purpose of demonstrating our work, we used examples of various liver illnesses. We used a disease dataset with a variety of symptoms as features to predict the disease in this paper. Before fitting the data into the Machine Learning model, various data pretreatment techniques and a filter-based feature selection strategy are employed on it. Three different supervised machine learning models, SOM, Back propagation and Ensemble model are used to train the data. The accuracy of these entire machine learning classifier models was evaluated and it was discovered that the ensemble Classifier produced the best results, with nearly 99.18 percent accuracy.