• Manish Tiwari
• Dr. Jyoti Singh Raghav
• Prof.(Dr.) Prasun Chakrabarti

Liver cancer is the most incurable disease in world. To save liver patient life accurate diagnosis is important. Based on experience of experts differences comes in diagnosis. This leads to patient life in risk. In this paper, artificial intelligence based machine learning classifiers such as lazy (RselibKnn, IBK, Kstar) classifier and function(LibSVM, Multilayerperceptron) classifier are useful to diagnosis of liver cancer accurately. These algorithms are implement using WEKA tool on Bupa liver disorder dataset. Analysis done on the base of Error evaluation such as Mean absolute error, Root mean squared error, Relative absolute error, root relative squared error and the performance such as accuracy, precision and recall of the classifier.

Lazy classifier, function classifier, WEKA, Error evaluation

"Discovered facts related to Liver disorder diagnosis using Lazy and Function classifiers", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.365-371, February-2019, Available :http://www.jetir.org/papers/JETIR1902853.pdf

"Discovered facts related to Liver disorder diagnosis using Lazy and Function classifiers", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp365-371, February-2019, Available at : http://www.jetir.org/papers/JETIR1902853.pdf