Ensemble of Multiple Classification Algorithms to Predict Stroke Dataset

Abstract

Machine learning algorithms became popular in many domains including applications in healthcare. However, in some cases, the datasets classifiers perform poorly due to several reasons. Studies have shown that combining classifiers may help improve performance and obtain better outcomes. Ensemble approach is a technique of combining two or more algorithms to make a robust system for predictions from all the base learners. Ensemble approach works based on supervised learning algorithms in which predictions of various learning algorithms are combined to make an estimation. The simplest type of ensemble learning is to train the base algorithms on random subsets of original data set and after that make a vote for by counting the most common classifications or by computing a form of averaging the predictions of the base algorithms. In this paper, various classifiers have been applied and compared for effective diagnosis of the Stroke data set. The Stroke dataset is used to demonstrate the effectiveness of the Ensemble approach for obtaining good predictions. Experimental results show that classifier Ensemble produces better prediction accuracy.