Data mining techniques and breast cancer prediction : A case study of Libya.

ABDULL, Mohamed A. Salem. (2011). Data mining techniques and breast cancer prediction : A case study of Libya. Doctoral, Sheffield Hallam University (United Kingdom)..

Preview

PDF (Version of Record) 10701258.pdf - Accepted Version All rights reserved. Download (41MB) | Preview

Abstract

Different forms of cancer have been widely studied and documented in various studies across the world. However, there have not been many similar studies in the developing countries - particularly those on the African continent (Parkin, et al., 2005). This thesis seeks to uncover the geo-demographic occurrence patterns of the disease by applying three Data mining Techniques, namely Logistic Regression (LR), Neural Networks (NNs) and Decision Trees (DTs), to learn the underlying rules in the overall behaviour of breast cancer. The data, 3,057 observations on 29 variables obtained from four cancer treatment centres in Libya (2004-2008), were interrogated using multiple K-folds cross validation. The predictive strategy yielded a list of breast cancer predictor factors ordered according to their importance in predicting the disease. Comparison between our results and those obtainable from conventional LR, NN and DT models shows that our strategy out-performs the conventional variable selection. It is expected that the findings from this thesis will provide an input into comparative geo-ethnic studies of cancer and provide informed intervention guidelines in the prevention and cure of the disease, not only in Libya but also in other parts of the world.

Item Type:	Thesis (Doctoral)
Contributors:	Thesis advisor - Ezepue, Patrick Thesis advisor - Mwitondi, Kassim Thesis advisor - Slack, Frances [0000-0001-6638-798X]
Additional Information:	Thesis (Ph.D.)--Sheffield Hallam University (United Kingdom), 2011.
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Sheffield Hallam Doctoral Theses
Depositing User:	EPrints Services
Date Deposited:	10 Apr 2018 17:22
Last Modified:	03 May 2023 02:06
URI:	https://shura.shu.ac.uk/id/eprint/20611

Actions (login required)

View Item

Downloads

Publication Metrics