Prediction model for recurrence of hepatocellular carcinoma after resection by using neighbor2vec based algorithms

Cao, Yuankui, Fan, Junqing, Cao, Hong, Chen, Yunliang, Li, Jie, Li, Jianxin and Zhang, Simin 2021, Prediction model for recurrence of hepatocellular carcinoma after resection by using neighbor2vec based algorithms, WIREs : Data Mining and Knowledge Discovery, vol. 11, no. 2, March/April 2021, doi: 10.1002/widm.1390.

Attached Files
Name Description MIMEType Size Downloads

Title Prediction model for recurrence of hepatocellular carcinoma after resection by using neighbor2vec based algorithms
Author(s) Cao, Yuankui
Fan, Junqing
Cao, Hong
Chen, Yunliang
Li, Jie
Li, JianxinORCID iD for Li, Jianxin orcid.org/0000-0002-9059-330X
Zhang, Simin
Journal name WIREs : Data Mining and Knowledge Discovery
Volume number 11
Issue number 2
Season March/April 2021
Article ID e1390
Total pages 13
Publisher John Wiley & Sons
Place of publication Oxford, Eng.
Publication date 2021-02-11
ISSN 1942-4787
1942-4795
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Computer Science
classification algorithm
hepatocellular carcinoma prediction model
neighbor2vec
Summary Liver cancer has become the third cause that leads to the cancer death. For hepatocellular carcinoma (HCC), as the highly malignant type of liver cancer, its recurrence rate after operation is still very high because there is no reliable clinical data to provide better advice for patients after operation. To solve the challenging issue, in this work, we design a novel prediction model for recurrence of HCC using neighbor2vec based algorithm. It consists of three stages: (a) In the preparation stage, the Pearson correlation coefficient was used to explore the independent predictors of HCC recurrence, (b) due to the low correlation between individual dimension and prediction target, K‐nearest neighbors (KNN) were found as a K‐vectors list for each patient (neighbor2vec), (c) all vectors lists were applied as the input of machine learning methods such as logistic regression, KNN, decision tree, naive Bayes (NB), and deep neural network to establish the neighbor2vec based prediction model. From the experimental results on the real data from Shandong Provincial Hospital in China, the proposed neighbor2vec based prediction model outperforms all the other models. Especially, the NB model with neighbor2vec achieves up to 83.02, 82.86, 77.6%, in terms of accuracy, recall rates, and precision.
Language eng
DOI 10.1002/widm.1390
Indigenous content off
Field of Research 0801 Artificial Intelligence and Image Processing
0804 Data Format
0806 Information Systems
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2020, Wiley Periodicals
Persistent URL http://hdl.handle.net/10536/DRO/DU:30147783

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 56 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Wed, 03 Feb 2021, 12:24:42 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.