Automatic detection of liver disease using voting ensemble method

Abstract

Live disease is one of the prominent causes of death which can be tackled by providing detection at an early stage along with possible countermeasures. Liver diseases caused by factors like genetic predisposition, infections and the environment. It requires diverse and targeted treatment options. The increasing of hepatic conditions worldwide is due to lifestyle actions such as intake of alcohol and drug with the consultation of physicians. The cause of numerous infections and disorders are not yet well understood. A voting ensemble method is proposed in this paper that considers influential factors responsible for liver disease. This predictive model aims to enhance forecasting reports with respect to other peer intelligent model. The enhanced efficiency reaches an accuracy of 77.2% which is quite promising towards early liver disease prediction.