An Evolutionary Algorithm for Heart Disease Prediction

M. A. Jabbar, B. L. Deekshatulu, Priti Chandra
  • January 2012, Springer Science + Business Media
  • DOI: 10.1007/978-3-642-31686-9_44

What is it about?

This Paper focuses a new approach for applying associa- tion rules in the Medical Domain to discover Heart Disease Predic- tion. The health care industry collects huge amount of health care data which,unfortunately are not mined to discover hidden information for effective decision making.Discovery of hidden patterns and relationships often goes unexploited. Data mining techniques can help remedy this situation.Data mining have found numerous applications in Business and Scientific domains.Association rules,classification,clustering are ma- jor areas of interest in data mining. Among these,association rules have been a very active research area.In our work Genetic algorithm is used to predict more accurately the presence of Heart Disease for Andhra Pradesh Population.The main motivation for using Genetic algorithm in the discovery of high level Prediction rules is that they perform a global search and cope better with attribute interaction than the greedy rule induction algorithms often used in Data Mining.

Why is it important?

In this paper we proposed a new method for heart disease prediction using genetic algorithm. Our proposed work will mine association rules from heart disease data more efficiently.The association rule mining problem is an NP hard problem because finding all frequent item sets having minimum support results in a search space of 2m which is exponential in m where m is number of item sets. We have applied genetic algorithm to optimize association rules. We obtain a fitness function for the task of optimizing the number of rules generated by efficiently pruning redundant Rules. In future work we plan to use different fitness functions for genetic algorithm for prediction of Heart Disease.

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http://dx.doi.org/10.1007/978-3-642-31686-9_44

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