Prediction of Heart Disease using Computational Algorithms

Nowadays, peoples are suffering from heart diseases steadily because of their ignorance towards their physical-fitness. Globally there are 32% population are suffering from heart disease by world health organization(WHO).The death rates are increasing because of heart attacks and even the peoples are suffering across the global for all kind of Genders because of heart problems. The cases of heart attack is increasing day-by-day. The prediction of heart diseases are very needful to the health sectors includes the hospitals, sanatoriums, nursing and medical because it is difficult to analyze the huge data. The better prediction of heart disease can prevent the life threats. There are many algorithms had used to predict the heart disease but In this paper many Machine Learning Classification algorithms are applied such as Logistic Regression, K-Nearest Neighbor(KNN), Decision tree-classifier, Random Forest, Naive Bayes and support vector machine. Here we have done the comparative study of all the algorithms mentioned in the above lines. This heart disease dataset is collected from kaggle.com. The objective of the paper is to find the better accuracy provided by the algorithm. Several outcomes has achieved and verified using accuracy and confusion matrix.