Periodicity: Bi Annual.
Impact Factor:
SJIF:5.079 & GIF:0.416
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded

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Published in:   Vol. 8 Issue 1 Date of Publication:   May 2019
Page(s):   07-10 Publisher:   Integrated Intelligent Research (IIR)
DOI:   10.20894/IJCOA. SAI : 2017SCIA316F0917

Nowadays healthcare field has additional data mining process became a crucial role to use for disease prediction. Data mining is that the process of investigate up info from the huge information sets. The medical information is extremely voluminous. Therefore the investigator is extremely difficult to predict the disease is challenging. To overcome this issue the researchers use data mining processing technique like classification, clustering, association rules so on. The most objective of this analysis work is to predict disease supported common attributes intake of alcohol, smoking, obesity, diabetes, consumption of contaminated food, case history of liver disease using classification algorithm. The algorithms employed in this analysis work are J48, Naive Bayes. These classification algorithms are compared base on the performance factors accuracy and execution time. The investigational results could be a improved classifier for predict the liver disease.