Term of Award

Summer 2018

Degree Name

Master of Science in Mathematics (M.S.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


Department of Mathematical Sciences

Committee Chair

Ionut Iacob

Committee Member 1

Stephen Carden

Committee Member 2

Divine Wanduku


World Health Organization statistics show that cervical cancer is the fourth most frequent cancer in women with an estimated 530,000 new cases in 2012. Cervical cancer diagnosis typically involves liquid-based cytology (LBC) followed by a pathologist review. The accuracy of decision is therefore highly influenced by the expert’s skills and experience, resulting in relatively high false positive and/or false negative rates. Moreover, given the fact that the data being analyzed is highly dimensional, same reviewer’s decision is inherently affected by inconsistencies in interpreting the data. In this study, we use an Artificial Neural Network based model that aims to considerably reduce experts’ inconsistencies in predicting cervical cancer. We rely on standard machine learning techniques to train the neural network using six experts’ predictions for cervical cancer (based on analysis of more than sixty parameters/risk factors) and we produce a model where the unanimous decision is predicted with very good accuracy.

Research Data and Supplementary Material