Application of Classification Algorithms for Diagnosis of Heart Disease with Few Different Characteristics of the Individuals
Classification algorithms like K-nearest neighbor, naive Bayes, and support vector machine are used for the classification of individuals upon given parameters. Application of these algorithms on heart disease dataset can have a significant response to the classification of new individuals whether they come under the category of disease or not. It is done through given parameters like Resting Blood pleasure, cholesterol in the dataset. In this paper, all three algorithms are applied to the heart disease dataset, and the behavior of all these classification algorithms are studied with the accuracy of the model.
Keywords: Classification, Neive Bayes, K-Nearest Neighbor, Support Vector Machine, Resting Blood Pleasure, Cholesterol.