PREDICTING BREAST CANCER SURVIVABILITY USING DATA MINING TECHNIQUES

Published 1 January 2006

Bassam A. Abdelghani, Eşref Oğuz Güven

Citations172

TL;DR

This paper investigated three data mining techniques: the Naive Bayes, the back-propagated neural network, and the C4.5 decision tree algorithms, and found out that C 4.5 algorithm has a much better performance than the other two techniques.

Abstract

In this paper we present an analysis of the prediction of survivability rate of breast cancer patients using data mining techniques. The data used is the SEER Public-Use Data. The preprocessed data set consists of 151,886 records, which have all the available 16 fields from the SEER database. We have investigated three data mining techniques: the Naive Bayes, the back-propagated neural network, and the C4.5 decision tree algorithms. Several experiments were conducted using these algorithms. The achieved prediction performances are comparable to existing techniques. However, we found out that C4.5 algorithm has a much better performance than the other two techniques.

Keywords

Computer ScienceHealth ProfessionsBiochemistry, Genetics and Molecular Biology

Elsevier eBooksData Mining: Practical Machine Learning Tools and Techniques

25,718 Citations2011Ian H. Witten, Eibe Frank

View PDF

C4.5: Programs for Machine Learning

23,665 Citations1992J. R. Quinlan

A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.

Analysis of Survival Data

5,999 Citations2018D. R. Cox, David Oakes

Programs for Machine Learning

5,794 Citations1994Steven L. Salzberg, Alberto M. Segre

In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments, which will be a welcome addition to the library of many researchers and students.

Artificial Intelligence in MedicinePredicting breast cancer survivability: a comparison of three data mining methods

1,216 Citations2004Dursun Delen, Glenn Walker +1 more

The comparative study of multiple prediction models for breast cancer survivability using a large dataset along with a 10-fold cross-validation provided us with an insight into the relative prediction ability of different data mining methods.

Artificial Intelligence in MedicineUniqueness of medical data mining

635 Citations2002Krzysztof J. Cios, G. William Moore

Ethical and legal aspects of medical data mining are discussed, including data ownership, fear of lawsuits, expected benefits, and special administrative issues.

PubMedThe surveillance, epidemiology, and end results program: a national resource.

585 Citations1999Benjamin F. Hankey, Lynn A. G. Ries +1 more

The fight against cancer escalated in 1971 with the introduction of the National Cancer Act during the presidency of Richard M. Nixon and since that time, there have been dramatic developments in a number of research areas including prevention, screening, and treatment.

IEEE Transactions on Information Technology in BiomedicineMedical diagnosis with c4.5 rule preceded by artificial neural network ensemble

181 Citations2003Zhihua Zhou, Yuan Jiang

Case studies on diabetes, hepatitis, and breast cancer show that C4.5 Rule-PANE could generate rules with strong generalization ability, which benefits from an artificial neural network ensemble, and strong comprehensibility, whichbenefits from rule induction.

OncologyArtificial Neural Networks Applied to Survival Prediction in Breast Cancer

158 Citations1999Johan Lundin, Johan Lundin +4 more

An artificial neural network is very accurate in the 5-, 10- and 15-year breast-cancer-specific survival prediction and the good predictive performance of a network trained without information on nodal status demonstrate that neural networks can be important tools for cancer survival prediction.

Artificial Intelligence ReviewMedical Data Mining on the Internet: Research on a Cancer Information System

49 Citations1999Andrea L. Houston, Hsinchun Chen +5 more

An architecture for medical knowledge information systems that will permit data mining across several medical information sources is proposed and a suite of data mining tools that are developing to assist NCI in improving public access to and use of the vast cancer information collections are discussed.