Masters Theses

Keywords and Phrases

Classification; Feature Selection; Machine Learning; Medicine; Neurology; Reinforcement Learning


“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to model the impact of altered pH on respiratory rate, based on the Henderson-Hasselbalch equation. The model behaves as expected and is a preliminary example of how reinforcement learning can be utilized for medical modelling. Its sophistication will be improved in future works”--Abstract, page iv.


Stanley, R. Joe
Hier, Daniel B.

Committee Member(s)

Corns, Steven


Electrical and Computer Engineering

Degree Name

M.S. in Computer Engineering


Missouri University of Science and Technology

Publication Date

Fall 2021

Journal article titles appearing in thesis/dissertation

  • Subsumption reduces dataset dimensionality without decreasing performance of a machine learning classifier
  • Utilizing reinforcement learning to generate an optimal policy for blood pH regulation
  • A comparison of three feature reduction strategies for high dimensionality neurological datasets


xi, 55 pages

Note about bibliography

Includes bibliographic references.


© 2021 Donald Coolidge Wunsch III, All rights reserved.

Document Type

Thesis - Open Access

File Type




Thesis Number

T 11969

Electronic OCLC #