Earticle

초록

영어

In computer aided diagnosis (CAD) process, one of the most challenging problems is data sparsity, which leads to the diagnosis results are not reliable. This paper proposes a clustering collaborative filtering based algorithm to solve the problem of data sparsity. In this paper, we use k-means clustering algorithm to cluster the same type of patients, and then adopt collaborative filtering method to fill the missing data values for each cluster, in this way to reduce the complexity of similarity calculation of collaborative filtering. The proposed method makes full use of the information-sharing mechanism of "similar patient population" to predict and fill the missing values. A hepatitis dataset is used for evaluating the performance of the algorithm. Results indicate that the proposed algorithm has better performance for medical record data sparsity problem.

목차

Abstract
 1. Introduction
 2. CAD Process based on Data Mining
  2.1 Process Description
  2.2 Processing of Missing Medical Record Data Values
 3. Algorithm Description
  3.1. K-means Clustering Algorithm
  3.2. Collaborative Filtering Algorithm
  3.3. K-means CF Algorithm
 4. The Results and Analysis of Experiment
  4.1. Dataset
  4.2. Evaluation Index of Experiment
  4.3. Experimental Results and Analysis
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Feng Xue-yuan [ School of Computer Science and Technology, Harbin University of Science and Technology, 150080 Harbin, China ]
• Li Peng [ School of Software, Harbin University of Science and Technology, 150080 Harbin, China, School of Computer Science and Technology, Harbin University of Science and Technology, 150080 Harbin, China ]
• Qiao Pei-li [ School of Computer Science and Technology, Harbin University of Science and Technology, 150080 Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605