Predictive analytic in health care using Case-based Reasoning (CBR)

• Main Authors: Letchmunan, Sukumar, Mansor, Zulkefli, Lee, Nikki Wan Yan, Low, Kah Meng, Tahir, Nur Farhana Izwani
• Format: Conference or Workshop Item
• Language: English
• Published: 2016
• Subjects: QA75 Electronic computers. Computer science; RA0421 Public health. Hygiene. Preventive Medicine
• Online Access: http://repo.uum.edu.my/22772/1/ICOCI%202017%208%2015.pdf; http://repo.uum.edu.my/22772/; http://icoci.cms.net.my/PROCEEDINGS/2017/Pdf_Version_Chap01e/PID24-8-15e.pdf

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.