A Hybrid Approach for Prediction and Stage Wise Classification of Liver Failure

Early stage disease prediction is an important research area in health sector and it used to helpful to give the required treatment on time. The different stages of liver failure classification are an import research to the society due to huge amount liver failure causes. The early stage of cirrhosis failure prediction reduces the risk of human life. In this research article we propose deep learning based techniques for prediction and classification using fatty liver. The new propose work is the combination of ensemble learning (EL), conventional neural network (CN) and belief neural network. So, the proposed method is called EL-CN. The EL used to predict the features and add the different features using combing all the features. The CNN is used to manage and classify the stage wise prediction and classification. The BNN is increase the accuracy and prediction rates with supporting features. The propose work EL-CN implemented using liver datasets. The liver dataset consists of MRI images and corresponding features. The propose work implemented using python programming language and used different metrics such as accuracy, specificity and sensitivity. Predicted outcomes evaluated with dominant existing works and produced better results in terms of metrics rates such as 98.8%, 98.6%, and 98.4% respectively.