A REVIEW ON THE DETECTION OF HEART ATTACK USING DATA MINING BY ACO TECHNIQUE

The goal of data mining is to extract knowledge from huge amount of data. Now a day's data mining technique used in the field of medical diagnose of critical diesis and clinical data. In this research propose model give a solution to predict heart diseases. In this paper proposes a novel approach of applying the Ant Colony Optimization technique (ACO) for extracting the Association Rules (AR) from the database to detect heart attack. This algorithm is broadly are many types of heart disease which are considered here Congenital Heart Disease Congestive Heart Failure Coronary Heart Disease. According to risk level identified we find the max pheromone value, max pheromone value is the addition of weight and the risk level. In the early days many research has been done in the field of considerable diseases like Heart Attack using various technologies like soft computing, Fuzzy Techniques and Data mining techniques. This study leads to make such kind of diseases efficiently identifiable and curable. So by this approach we can increase the detection probability in the early stage which is not generally detected in the earlier stage.