Earticle

초록

영어

The advent of the information age and the rapid development of IT skills have led to the construction of massive databases, thus the current research focus is shifting to the efficient utilization of these vast volumes of stored information. Among the data mining algorithms that have been applied to this problem, neural networks can be used with various types, qualities, distributions, or volumes of data and they have high predictive power. Thus, neural networks are known to be the most useful and extensible algorithms, whereas logistic analysis has many constraints. In addition, neural networks obtain better results when the assumptions of linear discriminant analysis cannot be satisfied. The present study evaluated a multilayer perceptron (MLP) and radial-basis function network (RBFN), and their performance levels were compared with logistic regression based on cross-validation using the same data. The experiments showed that MLP delivered better performance than other methods in medical diagnostic applications where numerical data are used. MLP also performed better with the heart disease dataset using finely specified data types compared with the diabetes dataset using simple data types.

목차

Abstract
 1. Introduction
 2. Neural Networks and Statistics
  2.1. MLP and RBFN
  2.2. Logistic Regression
  2.3. Filtering Modthods
 3. Experiments
  3.1. Previous Study Result
 4. Experiments
 5. Results
 6. Conclusion
 References

키워드

저자

• Hoon Jin [ Dept. of Computer Engineering, Sungkyunkwan University, South Korea ]
• Seungcheon Kim [ Dept. of information Communication Engineering, Hansung University, South Korea ] 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