Paper Title
A Survey of Machine Learning Algorithms for Early Prediction of Chronic Liver disease

Abstract
Timely projection of Liver diseases is the most strenuous task in modern world. The injurious liver seems to be treacherous and severe to the patient’s life. With the neoteric and extortionate treatments in the liver malady, it becomes imperative to recognize it in a prefatory stage. Machine learning algorithms come up with an environment to foretell the disease at an early stage becomes uncomplicated and life-sustaining. Machine Learning is the technological analysis of algorithms. An review is set down with regard to this paper concerning the researches performed like Hepatitis, Cirrhosis, Fatty Liver, Non-Fatty Liver, Liver Cancer etc. using various machine learning algorithms together with the boosting algorithms in machine learning such as AdaBoost, XGBoost, LogitBoost, Support vector Machine (SVM), Decision Tree(DT), Random Forest(RF), Naive Bayes(NB), Logistic regression(LR) and many more. Finally, the algorithms that are proved to be the pre-eminent in case of accuracy are raised. By means of these algorithms, streamlined and faultless prediction is achievable wherefore to save the patient’s life. Keywords - Machine Learning (ML), Liver Disease Diagnosis, Support Vector Machine (SVM), Decision Tree (DT) Algorithm, K-means.