An Efficient Data Mining and Ant Colony Optimization technique (DMACO) for Heart Disease Prediction

India is set to witness a spike in deaths due to heart diseases. Early stage detection may prevent the death due to the heart diseases. In this paper we provide an efficient approach which is based on Data Mining and Ant Colony Optimization technique (DMACO) for Heart Disease Prediction so that we can prevent it in the earlier stages. For this we first taken the concept of data mining to finding the support, generated support is used as a weight of the symptom which will be the initial pheromone value of the ant. Then we consider Pain in the chest, Discomfort radiating to the back, choking feeling (heartburn), Nausea, Extreme weakness and Irregular heartbeats as the factor of heart attack. According to risk level identified we find the max pheromone value), max pheromone value is the addition of weight and the risk level. After applying the DMACO algorithm we can observe the increasing detection rate. So by this approach we can increase the detection probability in the early stage which is not generally detected in the earlier stage.