Graduation Year

2025

Document Type

Thesis

Degree

M.S.C.S.

Degree Name

MS in Computer Science (M.S.C.S.)

Degree Granting Department

Computer Science and Engineering

Major Professor

John Templeton, Ph.D.

Committee Member

Shaun Canavan, Ph.D.

Committee Member

Seungbae Kim, Ph.D.

Committee Member

Nathan Schilaty, Ph.D.

Keywords

Data Visualization, Health Informatics, Machine Learning, Predictive Modeling, Statistical Analysis

Abstract

Concussions are a prevalent and complex medical condition requiring careful clinical assessment and data-driven insights for effective management. This thesis presents the development of an automated data analysis system for concussion patient records, integrating Flutter-based desktop application development with SQL-driven data processing. The system provides a streamlined, interactive interface for clincians and researchers to upload, visualize, and analyze patient data efficiently.

The proposed solution automates data cleaning, preprocessing, and statistical analysis, ensuring robust and reliable insights into demographic, clinical, and recovery-related factors. Key analyses include sex-based differences injury mechanisms, prior head injury impact, mood disorder correlations, and time-to-treatment variations. The system employs Levene’s Test, standard and Welch’s t-tests, ANOVA, and post-hoc analysis to derive meaningful statistical conclusions.

By leveraging Flutter for cross-platform development, the application offers an intuitive user experience while enabling real-time data exploration. The integration of SQL automates query execution and statistical processing, minimizing manual intervention and improving reproducibility. The system provides visual analytics to support clinicians in evidence-based decision-making, ultimately enhancing personalized treatment strategies for concussion patients.

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