A Survey on Heart Disease Early Prediction Methodologies

Abstract

In today health trend, the deaths are increasing due to the heart diseases. The lives to be saved and deaths need to be minimized by determining the heart disease on the samples of health patient sources derived from medical clinics. The appropriate treatments are to be guided and prescribed as the follow up. For predicting the heart diseases in advance, one of the factors significantly assumed is accuracy. Based on this factor, there were many methodologies are taken as study and compared with few factors. The review over those methodologies suggest that new methods are more sophisticated and are more reliable in determining the heart disease with more accuracy. The approaches that would be described in terms of their working theme and their accuracies are noted. The domains from which the techniques, the tools, the datasets are taken are from data mining, machine learning, deep learning and other type, python environment and other relevant type, Cleveland or Kaggle and other specified kind of data respectively. The accuracy of the described approaches are represented in a pictorial form.