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Today the heart disease is one of the most important causes of death in the world. So its early prediction and diagnosis is important in medical field, which could help in on time treatment, decreasing health costs and decreasing death caused by it. In fact the main goal of using data mining algorithms in medicine by using patients' data is better utilizing the database and discovering tacit knowledge to help doctors in better decision making.Therefore using data mining and discovering knowledge in cardiovascular centers could create a valuable knowledge, which improves the quality of service provided by managers, and could be used by doctors to predict the future behavior of heart diseases using past records. Also some of the most important applications of data mining and knowledge discovery in heart patients system includes: diagnosing heart attack from various signs and properties, evaluating the risk factors which increases the heart attack.In this article the effort focused on evaluating the previous works on discovering knowledge using data mining in heart diseases field, and also explain the used algorithms in every one of the previous works, to help the future researchers to gain maximum benefits from these abilities. Because of this, in the next sections, first we will explain various works in data mining field using heart patients' data, and will show the ability of data mining in various applications of heart disease field, and based on a table will show the history of data mining and it's applications in heart diseases field. Finally we will provide the best methods and algorithms used in various applications of heart diseases using a comparison and will show the results in a table. It is obvious in the diagrams that the suggested method has the best performance and best quality in prediction.

In this paper considering a n e w human gait recognition system based on Radon transform which gives a high precision recognition rate. Innovation of this paper allocate to feature extraction and usage of them during process by combined neural networks. feature extraction is based on the Radon transform of binary silhouettes .in this paper For each gait sequence, the transformed silhouettes are used after background estimation and human detection in the scene to make each related template's. then set of all templates is used to subspace projection by following PCA method and earning final decimated feature vector for each persons in database. consequently earned feature vector for each person's is applied to multilayer perceptron neural network and set of all neural networks feed to final neural network for final decision. Experimental results is performed over a suitable data base include 10 samples for ten person which each sample have 130 frames approximately. 97% recognition rate of the proposed system is obtained over 10 samples test patterns.