Classification model for heart disease prediction with feature selection through modified bee algorithm

Nowadays, a healthcare field produces a huge amount of data; for processing those data, some efficient techniques are required. In this paper, a classification model is developed for heart disease prediction and the attribute selection is carried out through a modified bee algorithm. The prediction of heart disease through models will help the practitioners to make a precise decision about patient health. Heart disease dataset is obtained from the UCI repository. Dataset consists of 76 features and all those seventy-six features have not contributed equal information during the time of classification. In the entire attributes, some of the attributes will contribute a large amount of information at the time of classification task and some of the attributes will contribute only a small amount of information during the classification task. In this paper, a modified bee algorithm is used to identify the best subset of attributes from the entire features in the dataset; i.e., the training phase of classification only retains those features that are contributing more information during classification task and it will reduce the training time of classifiers. The experiment is analyzed with an obtained reduced subset of attributes by using the following classifiers such as support vector machine, Naïve Bayes and KNN. The experimental result shows that the support vector machine classifier will provide a good classification accuracy, true positive rate, true negative rate, false positive rate and false negative rate compared to Naïve Bayes and KNN.