Morehead State University

Poster Title

Classification of Stroke Victims Through Ensemble Learning and an Exploration of Various Supervised Machine Learning Algorithms

Grade Level at Time of Presentation

Sophomore

Major

Computer Science

Minor

Mathematics

Institution

Morehead State University

KY House District #

93

KY Senate District #

31

Department

Computer Science

Abstract

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.

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Classification of Stroke Victims Through Ensemble Learning and an Exploration of Various Supervised Machine Learning Algorithms

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.