Title: IEDPS: intelligent elderly disease prediction system

Authors: Guoru Li; Hongzhen Zheng; Dianhui Chu; Chunshan Li

Addresses: Department of Computer Science and Technology, Harbin Institute of Technology, Weihai, Shandong, China ' Department of Computer Science and Technology, Harbin Institute of Technology, Weihai, Shandong, China ' Department of Computer Science and Technology, Harbin Institute of Technology, Weihai, Shandong, China ' Department of Computer Science and Technology, Harbin Institute of Technology, Weihai, Shandong, China

Abstract: This article implements the intelligent elderly disease prediction system in order to solve the problem that the existing disease prediction systems lack pertinence for the elderly. The elderly disease prediction model is optimised by adding specific attributes of the elderly based on the attributes selected by automatic attribute selection methods. We use accuracy, sensitivity and specificity to evaluate models' comprehensive performance. The results show that naive Bayes is better than logistic regression, which is more suitable for the intelligent elderly disease prediction system. The structure and class diagram of the system is designed. The intelligent elderly disease prediction system is implemented using the elderly disease prediction model based on naive Bayes.

Keywords: elderly disease prediction; specific attributes of the elderly; naive Bayes; logistic regression.

DOI: 10.1504/IJIMS.2018.091991

International Journal of Internet Manufacturing and Services, 2018 Vol.5 No.2/3, pp.232 - 244

Available online: 18 May 2018 *

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