Earticle

초록

영어

Ovarian cancer is very malignant tumor because it doesn’t have any striking symptoms in its early stages. That’s why the early screening is really necessary in its clinics. We try to look for the optimal methodology to find out biomarker combination making its classification performance better than other cases. We evaluate 9 machine learning algorithms, those are Random Forest, Logistic, Multilayer Perceptron, Bagging, Classification Via Regression, LogitBoost, MultiClassifer, Simple Logistic, and Logistic Regression. The Area Under the Curve (AUC) of each algorithm is compared. We firstly select 15 biomarkers which are widely spread in the ovarian cancer diagnosis and find the best three combinations which composed of two, three and four biomarkers by using Logistic Regression which is well known for its reliable performance. Than we re-evaluate the best combinations with nine algorithms including Logistic Regression to find the optimal machine learning algorithm. In this research, we can find possibility to use another machine learning algorithm rather than Logistic Regression.

목차

Abstract
 1. Introduction
 2. Data Set
 3. Experiment
 4. Results
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Hye-Jeong Song [ Dept. of Ubiquitous Computing, Hallym University, Bio-IT Research Center, Hallym University ]
• Seung-Kyun Ko [ Dept. of Ubiquitous Game Engineering, Hallym University, Bio-IT Research Center, Hallym University ]
• Jong-Dae Kim [ Dept. of Ubiquitous Computing, Hallym University, Bio-IT Research Center, Hallym University ]
• Chan-Young Park [ Dept. of Ubiquitous Computing, Hallym University, Bio-IT Research Center, Hallym University ]
• Yu-Seop Kim [ Dept. of Ubiquitous Computing, Hallym University, Bio-IT Research Center, Hallym University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605