Term of Award
Spring 2020
Degree Name
Master of Science, Information Technology
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Information Technology
Committee Chair
Hayden Wimmer
Committee Member 1
Cheryl Aasheim
Committee Member 2
Lei Chen
Abstract
Machine learning, data mining, and deep learning has become the methodology of choice for analyzing medical data and images. In this study, we implemented three different machine learning techniques to medical data and image analysis. Our first study was to implement different log base entropy for a decision tree algorithm. Our results suggested that using a higher log base for the dataset with mostly categorical attributes with three or more categories for each attribute can obtain a higher accuracy. For the second study, we analyzed mental health data tuning the parameters of the decision tree (splitting method, depth and entropy). Our results identified the most crucial attributes for the dataset. The final study is on the Kimia Path24 image dataset. We built and trained a deep convolutional neural network and tested different hypotheses of batch size, number of epoch and learning rate. For the final study, all the hypotheses were supported with our experimental results.
OCLC Number
1175589842
Catalog Permalink
https://galileo-georgiasouthern.primo.exlibrisgroup.com/permalink/01GALI_GASOUTH/1fi10pa/alma9916352093002950
Recommended Citation
Rahman, Shaikh Shiam, "Applying Artificial Intelligence to Medical Data" (2020). Electronic Theses and Dissertations. 2111.
https://digitalcommons.georgiasouthern.edu/etd/2111
Research Data and Supplementary Material
No
