http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : 현윤경(Yun Kyong Hyon) (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
Jaegeun Lee(이재근),Seung Woo Yang(양승우),Seunghee Lee(이승희),Yun Kyong Hyon(현윤경),Jinbum Kim(김진범),Long Jin(김용),Ji Yong Lee(이지용),Jong Mok Park(박종목),Taeyoung Ha(하태영),Ju Hyun Shin(신주현),Jae Sung Lim(임재성),Yong Gi 대한비뇨기종양학회 2019 대한비뇨기종양학회지 Vol.17 No.2
Purpose: The aim of this study was to evaluate the applicability of machine learning methods that combine data on age and prostate-specific antigen (PSA) levels for predicting prostate cancer. Materials and Methods: We analyzed 943 patients who underwent transrectal ultrasonography (TRUS)-guided prostate biopsy at Chungnam National University Hospital between 2014 and 2018 because of elevated PSA levels and/or abnormal digital rectal examination and/or TRUS findings. We retrospectively reviewed the patients’ medical records, analyzed the prediction rate of prostate cancer, and identified 20 feature importances that could be compared with biopsy results using 5 different algorithms, viz., logistic regression (LR), support vector machine, random forest (RF), extreme gradient boosting, and light gradient boosting machine. Results: Overall, the cancer detection rate was 41.8%. In patients younger than 75 years and with a PSA level less than 20 ng/mL, the best prediction model for prostate cancer detection was RF among the machine learning methods based on LR analysis. The PSA density was the highest scored feature importances in the same patient group. Conclusions: These results suggest that the prediction rate of prostate cancer using machine learning methods not inferior to that using LR and that these methods may increase the detection rate for prostate cancer and reduce unnecessary prostate biopsy, as they take into consideration feature importances affecting the prediction rate for prostate cancer.
-
-
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
