Heart Disease Prediction Using Logistic Regression and Random Forest Classifier

Abstract

Day by day, the cases of heart diseases are increasing at a rapid rate and it's very Important and concerning to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The work 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 two algorithms of machine learning, logistic regression and random forest classifier, 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 individual. The strength of the proposed model was quite satisfying and was able to predict evidence of having a heart disease in a particular individual by using random forest and logistic regression which showed a good accuracy. So a quite 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 work gives us significant knowledge that can help us predict the patients with heart disease or not.