Objectives: To predict outcome of patients with Subarachnoid Hemorrhage effectively by using novel ensemble classification method.
Methods: The different machine learning approaches are used to improve the outcome of patients with SAH prediction. One of such approach utilizes random forest classifier which is used for enhancing the prediction accuracy.
Findings: The outcome of patients with Subarachnoid Hemorrhage (SAH) prediction is helpful for guiding and caring patients. Such type of prediction is the most important in medical research area. Mostly SAH prediction is achieved by classification techniques such as decision rules, naive Bayesian classifiers, support vector machines, nearest neighbor classifiers and etc. However, these classifiers are not efficient for higher number of training cases.
Application/Improvements: In this paper, we propose a novel ensemble classification technique for effective classification. In which, a random forest classifier is introduced for providing efficient classification by integrating various machine learning algorithms. The algorithms used are C4.5, REPTree, and PART. The experimental results show that the best ensemble classifier and effectiveness of the random forest algorithm.