DOI:10.20894/IJDMTA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:4.893 & GIF:0.787
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
Data Mining extracts the knowledge or interesting information from large set of structured data that are from different sources. Data mining applications are used in a range of areas; they are financial data analysis, retail and telecommunication industries, banking, health care and medicine. In health care, the data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases that includes data preprocessing, classification, clustering, association rules and sequential patterns. This paper analyses the performance of two classification techniques such as Bayesian and Lazy classifiers for hepatitis and thyroiddataset.This classification task helps to classify the hepatitis dataset into two classes namely live and die and also to classify the thyroid dataset into two classes hyperthyroid or hypothyroid. In Bayesian classifier, two algorithms namely Bayes Net and Naive Bayes are considered. In Lazy classifier we used two algorithms namely IBK and KStar. Comparative analysis is done by using the WEKA tool. It is open source software which consists of the collection of machine learning algorithms for data mining tasks.
