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A Decision Support System for Predicting Heart Disease Using Multilayer Perceptron and Factor Analysis

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In the most recent decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. The medical diagnosis by nature is a complex and fuzzy cognitive process, and soft computing methods, such as neural networks, have indicated extraordinary potential to be applied in the development of the medical decision support systems (MDSS). Diagnosing of coronary disease is one of the critical issues to develop medical decision support system which will help the doctors to take viable decisions. Disease diagnosis can be solved by classification which is one of the vital techniques of Data mining. Neural Network has risen as an important tool for classification. In this paper, a multilayer perceptron based decision support system is developed to support the diagnosis of heart diseases. For diagnosis of heart disease significantly 13 attributes are used for this purpose. 95% classification accuracy has been obtained from the experiments performed on the data taken from heart disease database. The outcomes acquired shows that the designed diagnostic system is capable of predicting the risk level of heart disease effectively.
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Back Propagation; Factor Analysis; Heart Disease; Multilayer Perceptron; Prediction

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