TY  - JOUR
T1  - Assessing the Medical Data using Ranking Based Weighted Fuzzy Associative Classifier
AU - Nithya, N.S. AU - DuraiSwamy, K. 
JO  - International Journal of Soft Computing
VL  - 9
IS  - 1
SP  - 20
EP  - 26
PY  - 2014
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2014.20.26
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2014.20.26
KW  - Information gain
KW  -ranking based weight
KW  -heart disease prediction
KW  -breast cancer
KW  -medical data mining
KW  -fuzzy weighted support and confidence
KW  -fuzzy weighted association rule mining
AB  - Fuzzy Association Rule Mining algorithm is very efficient for diagnosis, prognosis 
  and treatment of diseases in medical field compared to other classification 
  technique. But it suffers from exponential growth of rules produced. Identifying 
  the most important risk factor is one of the main tasks in medical data mining. 
  To obtain these objectives the new algorithm using information gain ranking 
  based weight for fuzzy associative classification is proposed. The ranking of 
  attributes eliminates irrelevant attributes and assign weight value used for 
  assessing the risk factor of diseases. Elimination of irrelevant attributes 
  and ranking used to extract important rules in fuzzy association rule mining 
  which reduce the computation time and increase the classification accuracy. 
  The results are verified using the breast cancer dataset, heart diseases dataset 
  with different categories of attributes to demonstrate the effectiveness of 
  the proposed approach.
ER  -