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Applying Supervised Machine Learning Algorithms to Detect Cardiac Events

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Conference

2021 Fall ASEE Middle Atlantic Section Meeting

Location

Virtually Hosted by the section

Publication Date

November 12, 2021

Start Date

November 12, 2021

End Date

November 13, 2021

Page Count

7

Permanent URL

https://strategy.asee.org/38426

Download Count

63

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Paper Authors

biography

Eileen Deng Rye Country Day School

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Eileen Deng is a junior at Rye Country Day School, Class of 2023. Her areas of interest include many fields within science such as psychology–especially in personality–sociology, and computer science.

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Eunice Lee Townsend Harris High School

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Eunice Lee is a senior at Townsend Harris High School, class 2022. She has various interests within engineering and computer science, primarily in machine learning and finite element analysis.

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Daniel Shameti Midwood High School, Brooklyn, NY

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Daniel Shameti – Senior at Midwood High School, Brooklyn, NY. He is part of the Medical Science Program/Research track at Midwood High School. His interests are in biochemistry and research in the medical field.

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Yu Wang New York City College of Technology

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Dr. Wang received a doctoral degree in Electrical Engineering from the CUNY Graduate Center and joined the Department of Computer Engineering Technology at New York City College of Technology in 2009. Her research areas of interest are in engineering education, biomedical sensors, optoelectronics, modeling real-time systems, embedded system design, deep neural network and machine learning.

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Abstract

Within the realm of machine learning, numerous research advancements have enhanced the understanding of data analytics and prediction models. One of the more recent achievements in artificial intelligence is the rise of machine learning in healthcare, aiding in the development of streamlined treatment and diagnosis. Cardiac focus in this paper is due to an interest in how the pandemic restricted extracurriculars and athletics in school, which led to a decrease in physical activity in adolescents. With a decrease in physical activity, the cardiac systems of students might have weakened thus fostering an interest in applying machine learning to cardiac health in adolescents. By using wearable devices and mobile devices to collect data from participants (mainly adolescents), machine learning algorithms can be applied to the data and then analyzed to get information about the cardiac states of adolescents. Cardiac features were measured using the YAMAY Smart Watch wearable device; a variety of supervised machine learning algorithms (KNN, Naïve Bayes, Random Forest, and Decision Trees) were used to predict the expected data with the target data. Overall, after testing each of the supervised machine learning algorithms, Random Forest had the best prediction accuracy of 75.86%. With these results in mind, research focusing on applying supervised machine learning algorithms to detect cardiac events would benefit from using Random Forest.

Deng, E., & Lee, E., & Shameti, D., & Wang, Y. (2021, November), Applying Supervised Machine Learning Algorithms to Detect Cardiac Events Paper presented at 2021 Fall ASEE Middle Atlantic Section Meeting, Virtually Hosted by the section. https://strategy.asee.org/38426

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