Conference paper
Diagnosis of prostate cancer in a Chinese population by using machine learning methods
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.1-4
IEEE
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Honolulu, HI, USA, 17/07/2018–21/07/2018)
2018
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.
Details
- Title
- Diagnosis of prostate cancer in a Chinese population by using machine learning methods
- Authors/Creators
- G. Wang (Author/Creator)J.Y-C Teoh (Author/Creator)K-S Choi (Author/Creator)
- Publication Details
- 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.1-4
- Conference
- 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Honolulu, HI, USA, 17/07/2018–21/07/2018)
- Publisher
- IEEE
- Identifiers
- 991005543710407891
- Copyright
- ©2018 IEEE
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Conference paper
Metrics
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