Classification of Stroke Victims through Supervised Machine Learning Algorithms and Ensemble Learning

Throughout the developed and developing world, a malady has plagued humanity since antiquity: stroke. This research project explores several methodologies within machine learning that enable the classification of two distinct classes: absence or presence of a history of stroke. This paper explores several contemporary methodologies for classifying stroke victims using an aggregate of ensemble learning and a myriad of other supervised learning strategies. To this end, the research presented in this paper makes explicit use of bagging and boosting techniques to strengthen classification efficacy. Several supervised learning algorithms are utilized to perform the classification process. Python programming language with the machine learning libraries were used to design classification models and generate illustrative graphics. The experimental results, as discussed in greater detail in this paper, show that conditions such as stroke can be effectively classified with considerable high accuracy and precision.