Earticle

초록

영어

Recently, the number of people who visit the hospital due to diabetes is increasing. According to the Korean Diabetes Association, it is statistically indicated that one in seven adults aged 30 years or older in Korea suffers from diabetes, and it is expected to be more if the pre-diabetes, fasting blood sugar disorders, are combined. In the last study, the validity of Triglyceride and Cholesterol associated with diabetes was confirmed and analyzed using Random Forest. Random Forest has a disadvantage that as the amount of data increases, it uses more memory and slows down the speed. Therefore, in this paper, we compared and analyzed Random Forest and XGBoost, focusing on improvement of learning speed and prevention of memory waste, which are mainly dealt with in machine learning. Using XGBoost, the problem of slowing down and wasting memory was solved, and the accuracy of the diabetes recognition factor was further increased.

목차

Abstract
1. Introduction
2. Related Work
2.1 Bagging and Boosting
2.2 Bagging and Boosting
2.3 XGboost
3. Experiment and evaluation
3.1 Accuracy trend according to variable importance and number of data
3.2 Learning Speed and Accuracy by Algorithm
4. Conclusion
References

키워드

저자

• Yongsub Shin [ Graduate School of Smart Convergence Kwangwoon University, Seoul, Korea ]
• Dai Yeol Yun [ Professor, Department of Plasma Bioscience and Display, KwangWoon University, Seoul 01897, Korea ]
• Seok-Jae Moon [ Professor, Department of Computer Science, Kwangwoon University, Seoul, Korea ]
• Chi-gon Hwang [ Professor, Department of Computer Engineering, Institute of Information Technology, Kwangwoon University, Seoul, Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380