Prediction of Heart Diseases Using Machine Learning Approaches

Abstract

Now days, we can see the number of heart disease cases increasing highly. Especially old people affected by this. It is so much concerning for the world. We thought about this kind of disease that how we could predict this in advance. However, it is difficult to diagnosis so it can done correctly and quick too. We made a prediction system named heart diseases prediction system, which uses a patient's medical data to predict about the patient that he or not will be analysed about heart disease. The fundamental recognition of the studies paper on which sufferers are extra like to expand coronary heart sickness primarily base totally on numerous clinical characteristics. We used four Machine Learning algorithms to predict and classify heart disease patients such as Decision Tree, Logistic Regression, Random Forest Classifier and k-N Neighbor. To adjust how the version may be can enhance the accuracy about prediction of Heart Attack using some attributes in any individual. Therefore, a very much helpful approach have taken. The proposed model's power changed into pretty satisfying, because it changed into capable of are expecting proof of getting a coronary heart sickness in a particular person the use of Logistic Regression, Random forest and Decision Tree which showed a high level of accuracy when compared to k-N Neighbor. We just tried these four methods and we want to find which method would be more suitable among them for this kind of work.