Authors: Yanning Cao; Xiaoshu Zhang; Jin Wang
Addresses: Network Information Center, Binzhou Medical University, Yantai 264003, China ' School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China ' School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
Abstract: We are in an era of digital medicine in which physicians can generate copious patient data, but tools to analyse these data are limited. Thus, we used case data from patients with oesophageal cancer from a medical institution, removed incomplete information, and quantified the textual data according to recommendations from the corresponding physicians. We used different classification algorithms to process the data, predict patient survival, and compare accuracies across algorithms. Our experimental results show that the BayesNet algorithm was highly accurate and precise, and, thus, may represent a promising data-mining tool.
Keywords: data mining; classification algorithms; oesophageal cancer; BayesNet; digital medicine; patient data; patient survival.
International Journal of Computational Science and Engineering, 2020 Vol.22 No.2/3, pp.262 - 269
Received: 21 Mar 2019
Accepted: 22 Jul 2019
Published online: 18 May 2020 *