Award Date

12-1-2017

Degree Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science

First Committee Member

Kazem Taghva

Second Committee Member

Ajoy Datta

Third Committee Member

Laxmi Gewali

Fourth Committee Member

Emma Regentova

Number of Pages

97

Abstract

The purpose of this thesis is to propose the design and architecture of a testable, scalable, and ef-cient web-based application that models and implements machine learning applications in cancer prediction. There are various components that form the architecture of our web-based application including server, database, programming language, web framework, and front-end design. There are also other factors associated with our application such as testability, scalability, performance, and design pattern. Our main focus in this thesis is on the testability of the system while consid- ering the importance of other factors as well.

The data set for our application is a subset of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The application is implemented with Python as the back-end programming language, Django as the web framework, Sqlite as the database, and the built-in server of the Django framework. The front layer of the application is built using HTML, CSS and various JavaScript libraries.

Our Implementation and Installation is augmented with testing phase that include unit and functional testing. There are other layers such as deploying, caching, security, and scaling that will be briefly discussed.

Keywords

data science; machine learning; web application; web framework; web service

Disciplines

Computer Sciences

Language

English


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