Comparison of Classification Techniques For Diabetes Dataset Using Weka Tool
- Minal Ugale
- Darshana Patil
- Meghana Shah
Data Mining, Weka Tool, Classification, Naïve Bayes, J48 Tree, SMO, REP Tree, Random Tree
As we know that the most life threaten disease which is prevalent in most of the developing as well as in developed countries is nothing but the Diabetes. The data classification can be applied on the dataset of the diabetic patients which can be developed by collecting enormous amount of data from the hospital repository having 1086 instances along with different attributes. Urine tests and blood tests are two categories of tests which are the instances in the given dataset. In the following paper we discuss and compare various classification algorithms of data mining that have been utilized for diabetic disease prediction. In order to do the classification of diseases such as cancer and diabetes many of data mining techniques used by the world health organization one of the technique is the classification only. As data mining is the computer assisted process of digging into and then analyses the large extent sets of data and then extracting the meaningful data. Data mining tools predicts behaviors, future trends and allows making of proactive decisions by businesses.
Minal Ugale, Darshana Patil, Meghana Shah. "Comparison of Classification Techniques For Diabetes Dataset Using Weka Tool".INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH ISSN:2321-9939, Vol.4, Issue 2, pp.243-248, URL :https://rjwave.org/ijedr/papers/IJEDR1602042.pdf
Volume 4 Issue 2
Pages. 243-248
