A Computational Intelligence Method for Effective Diagnosis of Heart Disease using Genetic Algorithm

Abstract

In recent years improvement of new and effective medical domain applications has vital role in research. Computational Intelligence Systems (CIS) has profound influence in the enlargement of these effective medical field applications and tools. One of the prevalent diseases that world facing is heart disease. The Computational Intelligence Systems uses input clinical data from different knowledge resources throughout the world and applies this data on different computational intelligence tools that uses sophisticated algorithms. The sophisticated algorithms plays prominent role in the construction of medical clinical analysis tools. These tools may be used as an extra aid for the clinical diagnosis of the diseases for the doctors and clinicians. In this paper a novel method for the diagnosis of heart disease has been proposed using Genetic Algorithms. In this approach an effective association rules are inferred using Genetic Algorithm approach which uses tournament selection, crossover, mutation and new proposed fitness function. The Cleaveland data set is used for the experimentation. This data set is collected from the UCI machine learning repository experimental results are prominent when compared with some of the supervised learning techniques.