Term of Award
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 of Information Technology
Committee Member 1
Committee Member 2
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.
Rahman, Shaikh Shiam, "Applying Artificial Intelligence to Medical Data" (2020). Electronic Theses and Dissertations. 2111.
Research Data and Supplementary Material