The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE
Transactions ID Number: 54-163
Full Name: Asha T
Position: Associate Professor
Age: ON
Sex: Female
Address: Dept. of ISE, BIT,K R Road, V V puram Bangalore
Country: INDIA
Tel: 91-80-26690981
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E-mail address: asha.masthi@gmail.com
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Title of the Paper: Optimization of Association Rules for Tuberculosis using Genetic Algorithm
Authors as they appear in the Paper: Asha.T, S Natarajan, K.N.B. Murthy
Email addresses of all the authors: asha.masthi@gmail.com, natarajan@pes.edu, principal@pes.edu
Number of paper pages: 10
Abstract: Association rule mining is the process of discovering interesting and unexpected rules from large sets of data. This approach results in huge quantity of rules where some are interesting and others are repetitive. It also limits the quality of rules to only two measures support and confidence. Tuberculosis (TB) is a disease caused by bacteria called Mycobacterium Tuberculosis. It usually spreads through the air and attacks low immune bodies. Human Immuno deficiency Virus (HIV) patients are more likely to be attacked with TB. It is an important health problem in India as well. Diagnosis of pulmonary tuberculosis has always been a problem. In this paper we try to optimize the rules generated by Association rule mining (Apriori) using Genetic Algorithm. Our approach is to extract only a small set of high quality Tuberculosis rules from among the larger set using genetic algorithm. Hence genetic algorithm operators such as selection, crossover and mutation are appl!
ied on the original set of large TB association rules using an objective function that includes three measures such as support, confidence and lift. The experimental result includes a small set of converged TB rules that helps doctors in their diagnosis decisions. The main motivation for using Genetic algorithms in the discovery of high-level prediction rules is that they are robust, use adaptive search techniques that perform a global search on the solution space and cope better with attribute interaction than the greedy rule induction algorithms often used in data mining.
Keywords: Tuberculosis, Data Mining, Diagnosis, Association Rules, Optimization, Genetic Algorithm
EXTENSION of the file: .doc
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