Diagnosing for Liver Disease Prediction in Patients Using Combined Machine Learning Models

In the human body one of the most important organs is liver. If the regular functionality of the liver is disturbed then this condition is called disease affected liver. Therefore, an early stage of disease detection is more important which helps in disease prevention at starting stage with small medications. But, it is too difficult to identify Liver disease at early stages because symptoms are very less at the starting stage. Lab results with physical examination are involved in the Traditional methods. This paper aims to represent a Diagnosing for Liver disease prediction in Patients using Combined Machine Learning Models. Optimized three machine learning algorithms are used in accurate diagnosis of liver disease by the doctors and these are Artificial Neural Networks (ANN), Decision Trees, K-Nearest Neighbors (KNN). With the help of these algorithms, given data is classified and results are produced. The future data is predicted with the help of past and present data in these machine learning algorithms. The accuracy results are produced by comparing three classification algorithms.