Prediction of Liver Disease With Feature Selection & Without Feature Selection Algorithms Using Various Machine Learning Algorithms.

Abstract

In the recent times, the amount of human suffering from Liver disease is increasing rapidly. The Reason behind this is due to unhealthy lifestyle and excessive alcohol intake. The patients  suffering from Liver disease has grown rapidly in the recent times, so in order to be cautions, we have to come up with a prediction model for predicting whether a patient is suffering from Liver related diseases or not. So prediction of liver disease in early stage will save the human life. The dataset used in this paper consists of 10 predictive attributes and 1 class. The main content of this paper is to predict the liver disease using various classification algorithms with and without feature reduction and without feature reduction datasets. The performance measures such as precision, recall, f-measure, ROC area, MAE, RMSE, accuracy are considered and compared with and without feature selection.