Letchmunan, Sukumar and Mansor, Zulkefli and Lee, Nikki Wan Yan and Low, Kah Meng and Tahir, Nur Farhana Izwani (2016) Predictive analytic in health care using Case-based Reasoning (CBR). In: 6th International Conference on Computing & Informatics (ICOCI2017), 25 - 27 April 2017, Kuala Lumpur.

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Abstract

Big data analytics enables useful information to be extracted in order to predict trends and behavior patterns.Predictive analytics can be applied in health care industry by using the information gained from big data analytics.There are several methods to make predictive analytics. Casebased Reasoning (CBR) is one of the methods to make prediction on patients’ sickness based on previous experiences.There are several challenges when applying CBR to predictive analytics.This paper focuses on solving the number of analogies used when applying CBR.Experiments and calculations are done to compare the accuracy of the number of analogies used.The results shows one analogy has the highest accuracy as compared to two and three analogies.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	EISSN 2289-7402 E-ISBN 978-967-0910-33-8 Organized by: School of Computing, Universiti Utara Malaysia.
Uncontrolled Keywords:	prediction, big data, health care, case based reasoning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions:	School of Computing
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	26 Jul 2017 06:53
Last Modified:	26 Jul 2017 06:53
URI:	https://repo.uum.edu.my/id/eprint/22772

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