Data mining techniques in health informatics: a case study from breast cancer research

Jing Lu; Alan Hales; David Rew; Malcolm Keech; Christian Fröhlingsdorf; Alex Mills-Mullett; Christian Wette

doi:10.1007/978-3-319-22741-2_6

Data mining techniques in health informatics: a case study from breast cancer research

Jing Lu
, Alan Hales
, David Rew
, Malcolm Keech
, Christian Fröhlingsdorf
, Alex Mills-Mullett
, Christian Wette

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

10 Citations (Scopus)

Abstract

This paper presents a case study of using data mining techniques in the analysis of diagnosis and treatment events related to Breast Cancer disease. Data from over 16,000 patients has been pre-processed and several data mining techniques have been implemented by using Weka (Waikato Environment for Knowledge Analysis). In particular, Generalized Sequential Patterns mining has been used to discover frequent patterns from disease event sequence profiles based on groups of living and deceased patients. Furthermore, five models have been evaluated in Classification with the objective to classify the patients based on selected attributes. This research showcases the data mining process and techniques to transform large amounts of patient data into useful information and potentially valuable patterns to help understand cancer outcomes.

Original language	English
Title of host publication	Information Technology in Bio- and Medical Informatics 6th International Conference, ITBAM 2015, Valencia, Spain, September 3-4, 2015, Proceedings
Publisher	Springer
Pages	56-70
Volume	9267
ISBN (Print)	9783319227405
DOIs	https://doi.org/10.1007/978-3-319-22741-2_6
Publication status	Published - 11 Aug 2015

Publication series

Name	Lecture Notes in Computer Science
Number	9267

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Breast cancer datasets
Clinical data environment
Data mining techniques
Database technology
Health informatics
Knowledge discovery
electronic patient records

Access to Document

10.1007/978-3-319-22741-2_6

https://link.springer.com/chapter/10.1007%252F978-3-319-22741-2_6

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Lu, J., Hales, A., Rew, D., Keech, M., Fröhlingsdorf, C., Mills-Mullett, A., & Wette, C. (2015). Data mining techniques in health informatics: a case study from breast cancer research. In Information Technology in Bio- and Medical Informatics 6th International Conference, ITBAM 2015, Valencia, Spain, September 3-4, 2015, Proceedings (Vol. 9267, pp. 56-70). (Lecture Notes in Computer Science; No. 9267). Springer. https://doi.org/10.1007/978-3-319-22741-2_6

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abstract = "This paper presents a case study of using data mining techniques in the analysis of diagnosis and treatment events related to Breast Cancer disease. Data from over 16,000 patients has been pre-processed and several data mining techniques have been implemented by using Weka (Waikato Environment for Knowledge Analysis). In particular, Generalized Sequential Patterns mining has been used to discover frequent patterns from disease event sequence profiles based on groups of living and deceased patients. Furthermore, five models have been evaluated in Classification with the objective to classify the patients based on selected attributes. This research showcases the data mining process and techniques to transform large amounts of patient data into useful information and potentially valuable patterns to help understand cancer outcomes.",

keywords = "Breast cancer datasets, Clinical data environment, Data mining techniques, Database technology, Health informatics, Knowledge discovery, electronic patient records",

author = "Jing Lu and Alan Hales and David Rew and Malcolm Keech and Christian Fr{\"o}hlingsdorf and Alex Mills-Mullett and Christian Wette",

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}

Lu, J, Hales, A, Rew, D, Keech, M, Fröhlingsdorf, C, Mills-Mullett, A & Wette, C 2015, Data mining techniques in health informatics: a case study from breast cancer research. in Information Technology in Bio- and Medical Informatics 6th International Conference, ITBAM 2015, Valencia, Spain, September 3-4, 2015, Proceedings. vol. 9267, Lecture Notes in Computer Science, no. 9267, Springer, pp. 56-70. https://doi.org/10.1007/978-3-319-22741-2_6

Data mining techniques in health informatics: a case study from breast cancer research. / Lu, Jing; Hales, Alan; Rew, David et al.
Information Technology in Bio- and Medical Informatics 6th International Conference, ITBAM 2015, Valencia, Spain, September 3-4, 2015, Proceedings. Vol. 9267 Springer, 2015. p. 56-70 (Lecture Notes in Computer Science; No. 9267).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Data mining techniques in health informatics: a case study from breast cancer research

AU - Lu, Jing

AU - Hales, Alan

AU - Rew, David

AU - Keech, Malcolm

AU - Fröhlingsdorf, Christian

AU - Mills-Mullett, Alex

AU - Wette, Christian

PY - 2015/8/11

Y1 - 2015/8/11

N2 - This paper presents a case study of using data mining techniques in the analysis of diagnosis and treatment events related to Breast Cancer disease. Data from over 16,000 patients has been pre-processed and several data mining techniques have been implemented by using Weka (Waikato Environment for Knowledge Analysis). In particular, Generalized Sequential Patterns mining has been used to discover frequent patterns from disease event sequence profiles based on groups of living and deceased patients. Furthermore, five models have been evaluated in Classification with the objective to classify the patients based on selected attributes. This research showcases the data mining process and techniques to transform large amounts of patient data into useful information and potentially valuable patterns to help understand cancer outcomes.

AB - This paper presents a case study of using data mining techniques in the analysis of diagnosis and treatment events related to Breast Cancer disease. Data from over 16,000 patients has been pre-processed and several data mining techniques have been implemented by using Weka (Waikato Environment for Knowledge Analysis). In particular, Generalized Sequential Patterns mining has been used to discover frequent patterns from disease event sequence profiles based on groups of living and deceased patients. Furthermore, five models have been evaluated in Classification with the objective to classify the patients based on selected attributes. This research showcases the data mining process and techniques to transform large amounts of patient data into useful information and potentially valuable patterns to help understand cancer outcomes.

KW - Breast cancer datasets

KW - Clinical data environment

KW - Data mining techniques

KW - Database technology

KW - Health informatics

KW - Knowledge discovery

KW - electronic patient records

U2 - 10.1007/978-3-319-22741-2_6

DO - 10.1007/978-3-319-22741-2_6

M3 - Chapter

SN - 9783319227405

VL - 9267

T3 - Lecture Notes in Computer Science

SP - 56

EP - 70

BT - Information Technology in Bio- and Medical Informatics 6th International Conference, ITBAM 2015, Valencia, Spain, September 3-4, 2015, Proceedings

PB - Springer

ER -

Lu J, Hales A, Rew D, Keech M, Fröhlingsdorf C, Mills-Mullett A et al. Data mining techniques in health informatics: a case study from breast cancer research. In Information Technology in Bio- and Medical Informatics 6th International Conference, ITBAM 2015, Valencia, Spain, September 3-4, 2015, Proceedings. Vol. 9267. Springer. 2015. p. 56-70. (Lecture Notes in Computer Science; 9267). doi: 10.1007/978-3-319-22741-2_6

Data mining techniques in health informatics: a case study from breast cancer research

Abstract

Publication series

UN SDGs

Keywords

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