Vessel blocking is one of the reasons behind the death of people universally, more people pass away from cardiovascular diseases than from any other cause annually. To stay away from heart disease or to those symptoms early. Many experts will be developing intelligent decision support systems related to medical to get the better ability of the doctors in the detection of heart disease. In heart disease diagnosis and treatment, single data image are providing reasonable accuracy. The Heart Vessel blocking Prediction proposed system guides through an intelligent decision support system. In our proposed model a predictive analysis is carried out on Heart Disease Data using K-means and ANT colony optimization (ACO) techniques. Medical data is a combination of image and data set. This classification is implemented by developing a model using ANT colony optimization. This initial segmentation is refined by finding the orthogonal line on each ridge pixel of the vessel region. In this framework. The evaluation results prove that our method performs better in a much shorter time which can be verified in the mat lab environment. This section presents the simulation results for proposed Ant Colony Optimization Based Heart Disease Identification (ACO-HDI). A total of three simulations were conducted to evaluate the performance of the proposed approaches. In proposed model compare with two existing model they Are Particle Swarm Optimization with K-Means (PSOK)we evaluate a swarm intelligent K-algorithm for dental property diagnosis, a disease that is most commonly found at all age groups, and Artificial Fish Swarm Algorithm Based K-Means (AFSA)is the widely used K-Means technique. K-algorithms the performance of the algorithm depends on the availability of the original masonry centers and one for local refinance. The following metrics were adopted to evaluate the performance of the proposed schemes. Compare to PSOK, AFSA, ACO-HDI all other methods the accuracy will increased in proposed method, also give the better result for proposed method. Heart disease is a major life-threatening disease that causes to death and it has a serious long-term disability. The time taken to recover from heart disease depends on the patient’s severity. Heart disease diagnosis is a complex task which requires much experience and knowledge. Nowadays, the healthcare industry contains the huge amount of healthcare data, which contain hidden information we put the various vessel extraction approaches and techniques in perspective by means of a classification of the existing research. We have divided vessel segmentation algorithms and techniques into six main categories: (1) pattern recognition techniques, (2) model-based approaches, (3) tracking-based approaches, (4) artificial intelligence-based approaches, (5) neural network-based approaches, (6)miscellaneous tube-like object detection approaches. some of these categories are further divided into subcategories. We have also created tables to compare the papers in each category against such criteria as dimensionality, input type, pre-processing, user interaction, and result type.

Heart Vessel blocking Prediction, ANT Algorithm, k-means Clustering, MATLAB

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