Cardiovascular disease is one of the major causes of death across the globe and the detection of the disease at an early stage is of utmost importance. Of late, due to the development of technology, early and accurate detection of the disease has become possible and this in turn can help in reducing the risk of the disease and in reducing the number of patients affected. At the same time, diagnosis of the disease is the most critical as well as a complicated problem which has to be accomplished in an accurate and efficient manner. Heart disease data can be analyzed using a neural network approach. The primary goal of data mining is to find the relation between data as well as predicting the result. The prime data-mining technique to classify a provided set of input data is classification. Several practical issues which we face in our day-to-day life in various multifaceted disciplines from business to medicine can be tackled by classification approach. Classification process can be made more efficient by adopting parallel approach in the training phase. Optimization technique can be used for better classification and to improve accuracy. To optimize the weight of the Artificial Neural Network (ANN) structure, Group Search Optimizer (GSO) technique is used.
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