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 : Munetoshi Akazawa (검색결과 1 건)
- 무료
- 기관 내 무료
- 유료
-
Munetoshi Akazawa,Kazunori Hashimoto,Katsuhiko Noda,Kaname Yoshida 대한산부인과학회 2021 Obstetrics & Gynecology Science Vol.64 No.3
ObjectiveMost women with early stage endometrial cancer have a favorable prognosis. However, there is a subset of patientswho develop recurrence. In addition to the pathological stage, clinical and therapeutic factors affect the probabilityof recurrence. Machine learning is a subtype of artificial intelligence that is considered effective for predictive tasks. We tried to predict recurrence in early stage endometrial cancer using machine learning methods based on clinicaldata. MethodsWe enrolled 75 patients with early stage endometrial cancer (International Federation of Gynecology and Obstetricsstage I or II) who had received surgical treatment at our institute. A total of 5 machine learning classifiers were used,including support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and boostedtree, to predict the recurrence based on 16 parameters (age, body mass index, gravity/parity, hypertension/diabetic,stage, histological type, grade, surgical content and adjuvant chemotherapy). We analyzed the classification accuracyand the area under the curve (AUC). ResultsThe highest accuracy was 0.82 for SVM, followed by 0.77 for RF, 0.74 for LR, 0.66 for DT, and 0.66 for boosted trees. The highest AUC was 0.53 for LR, followed by 0.52 for boosted trees, 0.48 for DT, and 0.47 for RF. Therefore, the bestpredictive model for this analysis was LR. ConclusionThe performance of the machine learning classifiers was not optimal owing to the small size of the dataset. The useof a machine learning model made it possible to predict recurrence in early stage endometrial cancer.
KCI
KISS연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
