Day by day the cases of heart diseases are increasing at a rapid rate and it’s very significant
and concerning to predict any such diseases before hand. This diagnosis is a difficult task it
should be perform precisely and efficiently. The research paper mainly focuses on which
patient is more likely to have a heart disease based on various medical attributes. We prepared
a heart disease prediction system to predict whether the patient is likely to be diagnosed with
a heart disease or not using the medical history of the patient. We used different algorithms of
machine learning such as logistic regression and KNN to predict and classify the patient with
heart disease. A quite Helpful approach was used to regulate how the model can be used to
improve the accuracy of prediction of Heart Attack in any human being. The strength of the
proposed model was quiet agreeable and was able to predict evidence of having a heart disease
in a particular individual by using KNN and Logistic Regression which showed a good
accuracy in comparison to the previously used classifier such as naive bayes etc. So a quiet
significant amount of pressure has been lift off by using the given model in finding the
probability of the classifier to correctly and accurately identify the heart disease. The Given
heart disease prediction system enhances medical care and reduces the cost. This project gives
us significant knowledge that can help us predict the patients with heart disease.
| 