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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Overcoming Small Data Limitations In Heart Disease Prediction By Using Surrogate Data, Alfeo Sabay, Laurie Harris, Vivek Bejugama, Karen Jaceldo-Siegl
Overcoming Small Data Limitations In Heart Disease Prediction By Using Surrogate Data, Alfeo Sabay, Laurie Harris, Vivek Bejugama, Karen Jaceldo-Siegl
SMU Data Science Review
In this paper, we present a heart disease prediction use case showing how synthetic data can be used to address privacy concerns and overcome constraints inherent in small medical research data sets. While advanced machine learning algorithms, such as neural networks models, can be implemented to improve prediction accuracy, these require very large data sets which are often not available in medical or clinical research. We examine the use of surrogate data sets comprised of synthetic observations for modeling heart disease prediction. We generate surrogate data, based on the characteristics of original observations, and compare prediction accuracy results achieved from …
Machine Learning To Predict College Course Success, Anthony R.Y. Dalton, Justin Beer, Sriharshasai Kommanapalli, James S. Lanich Ph.D.
Machine Learning To Predict College Course Success, Anthony R.Y. Dalton, Justin Beer, Sriharshasai Kommanapalli, James S. Lanich Ph.D.
SMU Data Science Review
In this paper, we present an analysis of the predictive ability of machine learning on the success of students in college courses in a California Community College. The California Legislature passed assembly bill 705 in order to place students in non-remedial coursework, based on high school transcripts, to increase college completion. We utilize machine learning methods on de-identified student high school transcript data to create predictive algorithms on whether or not the student will be successful in college-level English and Mathematics coursework. To satisfy the bill’s requirements, we first use exploratory data analysis on applicable transcript variables. Then we use …
Walknet: A Deep Learning Approach To Improving Sidewalk Quality And Accessibility, Andrew Abbott, Alex Deshowitz, Dennis Murray, Eric C. Larson
Walknet: A Deep Learning Approach To Improving Sidewalk Quality And Accessibility, Andrew Abbott, Alex Deshowitz, Dennis Murray, Eric C. Larson
SMU Data Science Review
This paper proposes a framework for optimizing allocation of infrastructure spending on sidewalk improvement and allowing planners to focus their budgets on the areas in the most need. In this research, we identify curb ramps from Google Street View images using traditional machine learning and deep learning methods. Our convolutional neural network approach achieved an 83% accuracy and high level of precision when classifying curb cuts. We found that as the model received more data, the accuracy increased, which with the continued collection of crowdsourced labeling of curb cuts will increase the model’s classification power. We further investigated a model …