A performance comparison on the machine learning classifiers in predictive pathology staging of prostate cancer

Jae Kwon Kim; In Hye Yook; Mun Joo Choi; Jong Sik Lee; Yong Hyun Park; Ji Youl Lee; In Young Choi

doi:10.3233/978-1-61499-830-3-1273

A performance comparison on the machine learning classifiers in predictive pathology staging of prostate cancer

Jae Kwon Kim
, In Hye Yook
, Mun Joo Choi
, Jong Sik Lee
, Yong Hyun Park
, Ji Youl Lee
, In Young Choi

Inha University
Catholic Univ. of Korea Coll. Med.

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

14 Scopus citations

Abstract

This study objectives to investigate a range of Partin table and several machine learning methods for pathological stage prediction and assess them with respect to their predictive model performance based on Koreans data. The data was used SPCDB and gathered records from 944 patients treated with tertiary hospital. Partin table has low accuracy (65.68%) when applied on SPCDB dataset for comparison on patients with OCD and NOCD conditions. SVM (75%) represents a promising alternative to Partin table from which pathology staging can benefit.

Original language	English
Title of host publication	MEDINFO 2017
Subtitle of host publication	Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics
Editors	Adi V. Gundlapalli, Jaulent Marie-Christine, Zhao Dongsheng
Publisher	IOS Press BV
Pages	1273
Number of pages	1
ISBN (Electronic)	9781614998297
DOIs	https://doi.org/10.3233/978-1-61499-830-3-1273
State	Published - 2017
Event	16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 - Hangzhou, China Duration: 21 Aug 2017 → 25 Aug 2017

Publication series

Name	Studies in Health Technology and Informatics
Volume	245
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017
Country/Territory	China
City	Hangzhou
Period	21/08/17 → 25/08/17

Bibliographical note

Publisher Copyright:
© 2017 International Medical Informatics Association (IMIA) and IOS Press.

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Machine learning
Pathology staging
Prostate cancer

Access to Document

10.3233/978-1-61499-830-3-1273

Cite this

Kim, J. K., Yook, I. H., Choi, M. J., Lee, J. S., Park, Y. H., Lee, J. Y., & Choi, I. Y. (2017). A performance comparison on the machine learning classifiers in predictive pathology staging of prostate cancer. In A. V. Gundlapalli, J. Marie-Christine, & Z. Dongsheng (Eds.), MEDINFO 2017: Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics (pp. 1273). (Studies in Health Technology and Informatics; Vol. 245). IOS Press BV. https://doi.org/10.3233/978-1-61499-830-3-1273

Kim, Jae Kwon ; Yook, In Hye ; Choi, Mun Joo et al. / A performance comparison on the machine learning classifiers in predictive pathology staging of prostate cancer. MEDINFO 2017: Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics. editor / Adi V. Gundlapalli ; Jaulent Marie-Christine ; Zhao Dongsheng. IOS Press BV, 2017. pp. 1273 (Studies in Health Technology and Informatics).

@inproceedings{15e327562384468d8ff85784ec5240bb,

title = "A performance comparison on the machine learning classifiers in predictive pathology staging of prostate cancer",

abstract = "This study objectives to investigate a range of Partin table and several machine learning methods for pathological stage prediction and assess them with respect to their predictive model performance based on Koreans data. The data was used SPCDB and gathered records from 944 patients treated with tertiary hospital. Partin table has low accuracy (65.68\%) when applied on SPCDB dataset for comparison on patients with OCD and NOCD conditions. SVM (75\%) represents a promising alternative to Partin table from which pathology staging can benefit.",

keywords = "Machine learning, Pathology staging, Prostate cancer",

author = "Kim, \{Jae Kwon\} and Yook, \{In Hye\} and Choi, \{Mun Joo\} and Lee, \{Jong Sik\} and Park, \{Yong Hyun\} and Lee, \{Ji Youl\} and Choi, \{In Young\}",

note = "Publisher Copyright: {\textcopyright} 2017 International Medical Informatics Association (IMIA) and IOS Press.; 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 ; Conference date: 21-08-2017 Through 25-08-2017",

year = "2017",

doi = "10.3233/978-1-61499-830-3-1273",

language = "English",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press BV",

pages = "1273",

editor = "Gundlapalli, \{Adi V.\} and Jaulent Marie-Christine and Zhao Dongsheng",

booktitle = "MEDINFO 2017",

address = "Netherlands",

}

Kim, JK, Yook, IH, Choi, MJ, Lee, JS, Park, YH , Lee, JY & Choi, IY 2017, A performance comparison on the machine learning classifiers in predictive pathology staging of prostate cancer. in AV Gundlapalli, J Marie-Christine & Z Dongsheng (eds), MEDINFO 2017: Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 245, IOS Press BV, pp. 1273, 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017, Hangzhou, China, 21/08/17. https://doi.org/10.3233/978-1-61499-830-3-1273

A performance comparison on the machine learning classifiers in predictive pathology staging of prostate cancer. / Kim, Jae Kwon; Yook, In Hye; Choi, Mun Joo et al.
MEDINFO 2017: Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics. ed. / Adi V. Gundlapalli; Jaulent Marie-Christine; Zhao Dongsheng. IOS Press BV, 2017. p. 1273 (Studies in Health Technology and Informatics; Vol. 245).

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

TY - GEN

T1 - A performance comparison on the machine learning classifiers in predictive pathology staging of prostate cancer

AU - Kim, Jae Kwon

AU - Yook, In Hye

AU - Choi, Mun Joo

AU - Lee, Jong Sik

AU - Park, Yong Hyun

AU - Lee, Ji Youl

AU - Choi, In Young

PY - 2017

Y1 - 2017

N2 - This study objectives to investigate a range of Partin table and several machine learning methods for pathological stage prediction and assess them with respect to their predictive model performance based on Koreans data. The data was used SPCDB and gathered records from 944 patients treated with tertiary hospital. Partin table has low accuracy (65.68%) when applied on SPCDB dataset for comparison on patients with OCD and NOCD conditions. SVM (75%) represents a promising alternative to Partin table from which pathology staging can benefit.

AB - This study objectives to investigate a range of Partin table and several machine learning methods for pathological stage prediction and assess them with respect to their predictive model performance based on Koreans data. The data was used SPCDB and gathered records from 944 patients treated with tertiary hospital. Partin table has low accuracy (65.68%) when applied on SPCDB dataset for comparison on patients with OCD and NOCD conditions. SVM (75%) represents a promising alternative to Partin table from which pathology staging can benefit.

KW - Machine learning

KW - Pathology staging

KW - Prostate cancer

UR - https://www.scopus.com/pages/publications/85040518636

U2 - 10.3233/978-1-61499-830-3-1273

DO - 10.3233/978-1-61499-830-3-1273

M3 - Conference contribution

C2 - 29295358

AN - SCOPUS:85040518636

T3 - Studies in Health Technology and Informatics

SP - 1273

BT - MEDINFO 2017

A2 - Gundlapalli, Adi V.

A2 - Marie-Christine, Jaulent

A2 - Dongsheng, Zhao

PB - IOS Press BV

T2 - 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017

Y2 - 21 August 2017 through 25 August 2017

ER -

Kim JK, Yook IH, Choi MJ, Lee JS, Park YH , Lee JY et al. A performance comparison on the machine learning classifiers in predictive pathology staging of prostate cancer. In Gundlapalli AV, Marie-Christine J, Dongsheng Z, editors, MEDINFO 2017: Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics. IOS Press BV. 2017. p. 1273. (Studies in Health Technology and Informatics). doi: 10.3233/978-1-61499-830-3-1273

A performance comparison on the machine learning classifiers in predictive pathology staging of prostate cancer

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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