Categorical Data Analysis Commons^™

Atrial Fibrillation Management In Hispanic Adults, Tania Borja

Dissertations

Background: Research has found atrial fibrillation (AF) to be the primary or a contributing cause of death on 183,321 death certificates, and an underlying cause of death for 26,535 Americans in 2019. Findings indicate an increased AF diagnosis in White people compared to racial and ethnic minorities, contrasting widespread findings of increased prevalence of cardiovascular disease and ischemic strokes in minorities. Significant disparities—by race and socioeconomic status in disease distribution and access to testing and lifesaving treatments—have been documented, specifically associated with social determinants of health (SDOH); i.e., the conditions in which people are born, grow, live, work, and age. …

Payments Data In Gambling Research, Kasra Ghaharian, Mana Azizsoltani

International Conference on Gambling & Risk Taking

A considerable body of gambling-related research has leveraged gamblers' behavioral tracking data to address a broad set of research questions. These data have typically comprised of gamblers' betting-related behaviors including, for example, the frequency and volume of betting. The analysis of gamblers' payment-related behavioral data is far less common, but provides a fruitful avenue gambling-related research.

In this presentation we discuss a selection of potential research opportunities that payments transaction data presents. We supplement this discussion with specific analyses that have been performed by our research group. We also discuss knowledge gaps and areas for future research.

Utilizing New Technologies To Measure Therapy Effectiveness For Mental And Physical Health, Jonathan Ossie

Dissertations

Mental health is quickly becoming a major policy concern, with recent data reporting increasing and disproportionately worse mental health outcomes, including anxiety, depression, increased substance abuse, and elevated suicidal ideation. One specific population that is especially high risk for these issues is the military community because military conflict, deployment stressors, and combat exposure contribute to the risk of mental health problems.

Although several pharmacological approaches have been employed to combat this epidemic, their efficacy is mixed at best, which has led to novel nonpharmacological approaches. One such approach is Operation Surf, a nonprofit that provides nature-based programs advocating the restorative …

Identifying Key Activity Indicators In Rats' Neuronal Data Using Lasso Regularized Logistic Regression, Avery Woods

Honors Theses

This thesis aims to identify timestamps of rats’ neuronal activity that best determine behavior using a machine learning model. Neuronal data is a complex and high-dimensional dataset, and identifying the most informative features is crucial for understanding the underlying neuronal processes. The Lasso regularization technique is employed to select the most relevant features of the data to the model’s prediction. The results of this study provide insights into the key activity indicators that are associated with specific behaviors or cognitive processes in rats, as well as the effect that stress can have on neuronal activity and behavior. Ultimately, it was …

Employee Attrition: Analyzing Factors Influencing Job Satisfaction Of Ibm Data Scientists, Graham Nash

Symposium of Student Scholars

Employee attrition is a relevant issue that every business employer must consider when gauging the effectiveness of their employees. Whether or not an employee chooses to leave their job can come from a multitude of factors. As a result, employers need to develop methods in which they can measure attrition by calculating the several qualities of their employees. Factors like their age, years with the company, which department they work in, their level of education, their job role, and even their marital status are all considered by employers to assist in predicting employee attrition. This project will be analyzing a …

Crime In Los Angeles, Cierra Hughley

Symposium of Student Scholars

This study will examine crimes committed in the city of Los Angeles dating back to the year of 2020. The reported data was pulled from the open data of Los Angeles Police Department. The purpose of this study is to show if gender is related to the three primary crimes: property crimes, violent crimes, or other crimes. Doing so will show which crimes were committed by each gender. Even though this study is on gender and crimes committed; it was a hard decision because there were many variables to choose from. However, exploring the relationship between crime and gender was …

Open Data Indicates That Collegedale Could Be A Bluezone, Tristan Deschamps, Alva Johnson

Campus Research Day

A blue zone is an indicator of exceptional health in a community. Adventists have a blue zone community in Loma Linda, but there has been little research into other Adventist populated areas that could be blue zones. Therefore, our goal is to show that open data suggests that a blue zone may exist near Southern Adventist University, specifically in Collegedale. This data has been gathered from different federal sources, including, the CDC, the US Census Bureau, the Tennessee Department of Health, official state records, and federal documents that are available to the public.

The Effectiveness Of Visualization Techniques For Supporting Decision-Making, Cansu Yalim, Holly A. H. Handley

Modeling, Simulation and Visualization Student Capstone Conference

Although visualization is beneficial for evaluating and communicating data, the efficiency of various visualization approaches for different data types is not always evident. This research aims to address this issue by investigating the usefulness of several visualization techniques for various data kinds, including continuous, categorical, and time-series data. The qualitative appraisal of each technique's strengths, weaknesses, and interpretation of the dataset is investigated. The research questions include: which visualization approaches perform best for different data types, and what factors impact their usefulness? The absence of clear directions for both researchers and practitioners on how to identify the most effective visualization …

Mlb 2023 Season Attendance Predictions, Sophia Andersen, Anna Tollette, Hannah Clinton

Research and Scholarship Symposium Posters

The goal of this project was to predict home game attendance for all 30 Major League Baseball (MLB) teams in their 2023 season. Researching and understanding that data as well as identifying influential factors of attendance were key factors before building a predictive model. Both the given material and data sets from MinneMUDAC, the competition organizer, was used as well as some outside sources. Finally, a predictive model was coded in Python which gave attendance predictions for every MLB game scheduled in 2023. From these results, insights could be offered to Major League Baseball or each team individually, to help …

That’S My Deity: An Examination Of Online Lokean Cultures Through Log-Linear Modeling, Mary Bernstein

Senior Theses

A rise in online religious communities and the growth of so-called ‘Old World’ religions are reflected in the internet’s subcultures of Neopaganism, a growing religious movement that has been documented in America since the 1960s. The religions under this umbrella movement vary drastically and include belief systems such as Wicca, Druidry, and deity worship. Belief systems under this movement lack the traditional hierarchy found in structured religion and lack a singular sacred text. As such, believers usually find and support one another not through a physical sacred place of meeting, but through an online community that acts as sacred space. …

Prevalence Of Sars-Cov-2 Antibodies In Liberty University Student Population, Emily Bonus

Senior Honors Theses

In 2020, the virus SARS-CoV-2 gained attention as it spread around the world. Its antibodies are poorly understood, and little research focuses on those with few COVID-19 complications yet large numbers of close contacts: university students. This longitudinal study recorded SARS-CoV-2 antibody presence in 107 undergraduate Liberty University students twice during early 2021. After extensive data cleaning and the application of various statistical tests and ANOVAs, the data seems to show that in the case of COVID-19 infections, SARS-CoV-2 IgM antibodies are immediately produced, and then IgG antibodies follow later. However, the COVID-19 vaccine causes the production of both IgM …

Influence Diagnostics For Generalized Estimating Equations Applied To Correlated Categorical Data, Louis Vazquez

Statistical Science Theses and Dissertations

Influence diagnostics in regression analysis allow analysts to identify observations that have a strong influence on model fitted probabilities and parameter estimates. The most common influence diagnostics, such as Cook’s Distance for linear regression, are based on a deletion approach where the results of a model with and without observations of interest are compared. Here, deletion-based influence diagnostics are proposed for generalized estimating equations (GEE) for correlated, or clustered, nominal multinomial responses. The proposed influence diagnostics focus on GEEs with the baseline-category logit link function and a local odds ratio parameterization of the association structure. Formulas for both observation- and …

Fraud Pattern Detection For Nft Markets, Andrew Leppla, Jorge Olmos, Jaideep Lamba

SMU Data Science Review

Non-Fungible Tokens (NFTs) enable ownership and transfer of digital assets using blockchain technology. As a relatively new financial asset class, NFTs lack robust oversight and regulations. These conditions create an environment that is susceptible to fraudulent activity and market manipulation schemes. This study examines the buyer-seller network transactional data from some of the most popular NFT marketplaces (e.g., AtomicHub, OpenSea) to identify and predict fraudulent activity. To accomplish this goal multiple features such as price, volume, and network metrics were extracted from NFT transactional data. These were fed into a Multiple-Scale Convolutional Neural Network that predicts suspected fraudulent activity based …

A Review On Derivative Hedging Using Reinforcement Learning, Peng Liu

Research Collection Lee Kong Chian School Of Business

Hedging is a common trading activity to manage the risk of engaging in transactions that involve derivatives such as options. Perfect and timely hedging, however, is an impossible task in the real market that characterizes discrete-time transactions with costs. Recent years have witnessed reinforcement learning (RL) in formulating optimal hedging strategies. Specifically, different RL algorithms have been applied to learn the optimal offsetting position based on market conditions, offering an automatic risk management solution that proposes optimal hedging strategies while catering to both market dynamics and restrictions. In this article, the author provides a comprehensive review of the use of …

Analyzing Relationships With Machine Learning, Oscar Ko

Dissertations, Theses, and Capstone Projects

Procedurally, this project aims to take a dataset, analyze it, and offer insights to the audience in an easy-to-digest format. Conceptually, this project will seek to explore questions like: “Do couples that meet through online dating or dating apps have higher or lower quality relationships?”, “Can any features in this dataset help predict how a subject would rate their relationship quality?”, and “What other insights can I derive from using machine learning for exploratory analysis?” The intended audience for this project is anyone interested in romantic relationships or machine learning.

The dataset is from a Stanford University survey, “How Couples …

Automated Machine Learning: Intellient Binning Data Preparation And Regularized Regression Classfier, Jianbin Zhu

Electronic Theses and Dissertations, 2020-

Automated machine learning (AutoML) has become a new trend which is the process of automating the complete pipeline from the raw dataset to the development of machine learning model. It not only can relief data scientists' works but also allows non-experts to finish the jobs without solid knowledge and understanding of statistical inference and machine learning. One limitation of AutoML framework is the data quality differs significantly batch by batch. Consequently, fitted model quality for some batches of data can be very poor due to distribution shift for some numerical predictors. In this dissertation, we develop an intelligent binning to …

Knowledge Discovery On The Integrative Analysis Of Electrical And Mechanical Dyssynchrony To Improve Cardiac Resynchronization Therapy, Zhuo He

Dissertations, Master's Theses and Master's Reports

Cardiac resynchronization therapy (CRT) is a standard method of treating heart failure by coordinating the function of the left and right ventricles. However, up to 40% of CRT recipients do not experience clinical symptoms or cardiac function improvements. The main reasons for CRT non-response include: (1) suboptimal patient selection based on electrical dyssynchrony measured by electrocardiogram (ECG) in current guidelines; (2) mechanical dyssynchrony has been shown to be effective but has not been fully explored; and (3) inappropriate placement of the CRT left ventricular (LV) lead in a significant number of patients.

In terms of mechanical dyssynchrony, we utilize an …

Impacts Of Covid-19 On Industrial Growth In The United States, Emily G. Warthman, Charles J. Landis

Williams Honors College, Honors Research Projects

COVID-19 has caused massive ramifications on all parts of life in the world and industry growth/decline is not immune to it. This report will analyze nine different industries’ profit and revenue from quarterly data during the years 2009-2022. Forecast models will be generated using various methods and different techniques of validating to predict the values from Q2 2020- Q4 2022 based on historical data. After which, a comparison will be conducted between those predicted values to the actual average revenue and profit generated by order of greatest error percentage made. Thorough research will then be completed to determine if there …

A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu Phd, Irene Moulitsas Phd

Journal of Aviation/Aerospace Education & Research

This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features …

Predictors Of Covid-19 Vaccination Rate In Usa: A Machine Learning Approach, Syed M. I. Osman, Ahmed Sabit

WCBT Faculty Publications

In this study, we examine state-level features and policies that are most important in achieving a threshold level vaccination rate to curve the effects of the COVID-19 pandemic. We employ CHAID, a decision tree algorithm, on three different model specifications to answer this question based on a dataset that includes all the states in the United States. Workplace travel emerges as the most important predictor; however, the governors’ political affiliation (PA) replaces it in a more conservative feature set that includes economic features and the growth rate of COVID-19 cases. We also employ several alternative algorithms as a robustness check. …