Diagnosis of prostate cancer in a Chinese population by using machine learning methods

Guanjin Wang, Jeremy Yuen Chun Teoh, Kup Sze Choi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

An early diagnosis of prostate cancer (PC) is key for the successful treatment. Although invasive prostate biopsies can provide a definitive diagnosis, the number of biopsies should be reduced to avoid side effects and risks especially for the men with the low risk of cancer. Therefore, an accurate model is in need to predict PC with the aim of reducing unnecessary biopsies. In this study, we developed predictive models using four machine learning methods including Support Vector Machine (SVM), Least Squares Support Vector Machine (LS-SVM), Artificial Neural Network (ANN) and Random Forest (RF) to detect PC cases using available prebiopsy information. The models were constructed and evaluated on a cohort of 1625 Chinese men with prostate biopsies from Hong Kong hospital. All the models have the excellent performances in detecting significant PC cases, with ANN achieving the highest accuracy of 0.9527 and the AUC value of 0.9755. RF outperformed the other three methods in classifying benign, significant and insignificant PC cases, with an accuracy of 0.9741 and a F1 score of 0.8290.

Original languageEnglish
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781538636466
DOIs
Publication statusPublished - 26 Oct 2018
Event40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States
Duration: 18 Jul 201821 Jul 2018

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2018-July
ISSN (Print)1557-170X

Conference

Conference40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
CountryUnited States
CityHonolulu
Period18/07/1821/07/18

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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