Published Paper Details:

Healthcare is an inevitable task to be done in humans daily life. Cardiovascular disease is a category for a range of diseases that are affecting heart and blood vessels. The early methods of forecasting the cardiovascular diseases helpful in making decisions about the changes to have occurred in high-risk patients which results in the reduction of risks. The health care industry contains medical data, therefore machine learning algorithms are required to make the decisions effectively in the prediction of heart diseases. Recently research has delved into uniting these techniques to provide hybrid machine learning algorithms. In the proposed research, data pre-processing uses techniques like the removal of noisy data, removal of missed data, filling default value if applicable and classification of attributes for prediction and decision making at different levels. The performance of the diagnosis model is obtained by using the methods like classification, accuracy, sensitivity analysis. This project proposes a prediction model to predict whether a people have any heart disease or not and to provide an diagnosis on that. This is done basically by comparing the accuracies of applying rules to the individual results of Support Vector Machine, Gradient Boosting, Random forest, Naive Bayes classifier and logistic regression on the dataset taken in a region to present an accurate model of predicting cardiovascular disease