Author : Shreya B. Yadav Prof. M.S.Takalikar

There have been large scale advancements in the medical field that have contributed to reduced infant mortality and increased life expectancy. The increased life expectancy has had a double-edged effect, where there has been a sharp increase in the number of elderly individuals in the populous. This largescale increase in elderly individuals corresponds to a similar increase in the Heart Failure scenarios. Congestive Heart Failure or CHF is characterized by decreased blood flow which is due to the inability of the heart to provide enough blood to the various parts of the body due to extensive aging. The diagnosis of a heart failure condition is highly complex as there is no one definitive cause of this ailment rather a combination of different conditions. Therefore, the analysis technique that is used mostly for this purpose is the ECG or Electrocardiogram, through which the RR intervals that indicate the significant signs of Congestive Heart Failure. Therefore, for the purpose of automatic Heart failure prediction, the methodology described in this paper utilizes, Kmeans Clustering, Linear Regression, Artificial Neural Networks (ANN) and Dempster Shafer rules for the purpose of effective and accurate predictions.

Keywords: Congestive Heart Disease, K means clustering, Artificial Neural Network, Dempster Shafer Theory.