Prediction of heart disease using a hybrid technique in data mining classification

Heart disease prediction is treated as most complicated task in the field of medical sciences. Thus there arises a need to develop a decision support system for detecting heart disease of a patient. In this paper, we propose efficient genetic algorithm hybrid with the back propagation technique approach for heart disease prediction. Today medical field have come a long way to treat patients with various kind of diseases. Among the most threatening one is the Heart disease which cannot be observed with a naked eye and comes instantly when its limitations are reached. Bad clinical decisions would cause death of a patient which cannot be afforded by any hospital. To achieve a correct and cost effective treatment computer-based and support Systems can be developed to make good decision. Many hospitals use hospital information systems to manage their healthcare or patient data. These systems produce huge amounts of data in the form of images, text, charts and numbers. Sadly, this data is rarely used to support the medical decision making. There is a bulk of hidden information in this data that is not yet explored which give rise to an important query of how to make useful information out of the data. So there is necessity of creating an excellent project which will help practitioners predict the heart disease before it occurs. The main objective of this paper is to develop a prototype which can determine and extract unknown knowledge (patterns and relations) related with heart disease from a past heart disease database record. It can solve complicated queries for detecting heart disease and thus assist medical practitioners to make smart clinical decisions which traditional decision support systems were not able to. By providing efficient treatments, it can help to reduce costs of treatment.