A novel approach using Machine Learning Algorithm on healthy liver

Liver disease is treated as one of the most serious areas of concern in medicine not only in India but also across the world. To improve the competence of diagnosis at the preliminary level of disease accurately, we have taken the help of machine learning algorithms which is part of Artificial intelligence. In this paper, Different Machine Learning Algorithms like SVM, Naive Bayes, MLP, and KE Sieve classifier algorithms are used for liver disease prediction. The main objective of this study is to investigate and compare different classification algorithms for analyzing liver disease prediction, given Liver Function Test (LFT) dataset. Comparisons of these algorithms are being performed based on accuracy. From the experimental results, KES which is basically a non-iterative algorithm is observed as the best algorithm.