Predicting the missing value in a knowledge based system using Bayesian classification technique

When machine learning algorithms are applied to data collected from the huge amount of data in the universe, it is generally accepted that the data has not been consistently collected. The absence of expected data elements is common and the mechanism through which a data element is missing often involves the informative relevance of that data element in a specific purpose. Therefore, the absence of data may have information value of its own. In the process of designing an application intended to support a heart diseases system where we can predict the probability of heart attack of a patient on basis upon certain condition. Bayesian Classification is commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to predict the missing value in a database, it is an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. In applying this we will be able to predict whether the data is present in the database or not and give some idea about the probability of heart-attack to the patient.