Abstract
Now-a-days 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 classification. In the entire attributes, some of the attributes have contributed a large amount of information at the time of classification and some of the attributes have contributed 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 features from the entire features in the dataset i.e., in the training phase of classification only retain those features that are contributing more information during classification and it will reduce the training time of classifiers. The experiment is analyzed with a obtained reduced subset of features by using the following classifiers such as Support Vector Machine, Navie bayes, Decision tree and Random forest. 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 Navie bayes and Random forest tree classifier.
Multi-attribute decision making approach for placement of dynaflow controllers in a power system network using particle mobility honey bee algorithm
