A Testing of Case-Base Reasoning for Covid-19 Patient Status Confirmation

Salamun Salamun; Diki Arisandi; Luluk Elvitaria; Liza Trisnawati

doi:10.24014/ijaidm.v4i2.11990

A Testing of Case-Base Reasoning for Covid-19 Patient Status Confirmation

Salamun Salamun, Diki Arisandi, Luluk Elvitaria, Liza Trisnawati

Abstract

Currently, the world is facing a global pandemic that attacks all countries. In Indonesia, there are three types of status for suspected patients: asymptomatic person, Person Under Supervision, and Patient Under Supervision. The statuses are issued by a paramedic, conducting medical examinations or direct interviews with patients with several criteria. We conducted several non-medical experiments to assist medical personnel in determining the asymptomatic. We exploit the case-based reasoning (CBR) for determining the suspected patients, and the K-NN (K-Nearest Neighbor) for data grouping based on the level of similarity. The patients will be interviewed regarding their travel history, direct contact history, health status, and some other information for the past 14 days. This combination delivers the information of the similarity level from the given data and previous data. As a conclusion, the percentage level of similarity can be used by a medical officer to issue the status of patients and giving several recommendations to follow health protocols.

Keywords

case-based reasoning (CBR), K-NN alghoritm, pandemic

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References

W. Wu et al., “Psychological stress of medical staffs during outbreak of COVID-19 and adjustment strategy,” J. Med. Virol., vol. 92, no. 10, pp. 1962–1970, 2020.

L. Rivett et al., “Screening of healthcare workers for SARS-CoV-2 highlights the role of asymptomatic carriage in COVID-19 transmission,” eLife, vol. 9, pp. 1–20, 2020.

H. Wang, S. Wang, and K. Yu, “COVID-19 infection epidemic: The medical management strategies in Heilongjiang Province, China,” Crit. Care, vol. 24, no. 1, pp. 10–13, 2020.

G. Soldati et al., “Is There a Role for Lung Ultrasound During the COVID-19 Pandemic?,” J. Ultrasound Med., vol. 39, no. 7, pp. 1459–1462, 2020.

H. Harenčárová, “Managing Uncertainty in Paramedics’ Decision Making,” J. Cogn. Eng. Decis. Mak., vol. 11, no. 1, pp. 42–62, 2017.

R. T. Davey et al., “A randomized, controlled trial of ZMapp for ebola virus infection,” N. Engl. J. Med., vol. 375, no. 15, pp. 1448–1456, 2016.

D. Gu, C. Liang, and H. Zhao, “A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis,” Artif. Intell. Med., vol. 77, pp. 31–47, 2017.

M. B. Bentaiba-Lagrid, L. Bouzar-Benlabiod, S. H. Rubin, T. Bouabana-Tebibel, and M. R. Hanini, “A case-based reasoning system for supervised classification problems in the medical field,” Expert Syst. Appl., vol. 150, p. 113335, 2020.

N. C. Wong, C. Lam, L. Patterson, and B. Shayegan, “Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy,” BJU Int., vol. 123, no. 1, pp. 51–57, 2019.

Ž. Kovačević, L. Gurbeta Pokvić, L. Spahić, and A. Badnjević, “Prediction of medical device performance using machine learning techniques: infant incubator case study,” Health Technol. (Berl)., vol. 10, no. 1, pp. 151–155, 2020.

A. K. Goel and B. Diaz-Agudo, “What’s hot in case-based reasoning,” in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017, pp. 5067–5069.

H. Y. A. Abutair and A. Belghith, “Using Case-Based Reasoning for Phishing Detection,” Procedia Comput. Sci., vol. 109, pp. 281–288, 2017.

A. Rahman, C. Slamet, W. Darmalaksana, Y. A. Gerhana, and M. A. Ramdhani, “Expert System for Deciding a Solution of Mechanical Failure in a Car using Case-based Reasoning,” IOP Conf. Ser. Mater. Sci. Eng., vol. 288, no. 1, 2018.

J. Maillo, S. Ramírez, I. Triguero, and F. Herrera, “kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data,” Knowledge-Based Syst., vol. 117, pp. 3–15, 2017.

O. ten Cate, E. J. F. M. Custers, and S. J. Durning, Principles and practice of case-based clinical reasoning education: a method for preclinical students. Springer Nature, 2017.

B. Shi and S. S. Iyengar, Mathematical Theories of Machine Learning-Theory and Applications. Springer, 2020.

Q. Li et al., “Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia,” N. Engl. J. Med., vol. 382, no. 13, pp. 1199–1207, 2020.

P. Mehta, D. F. McAuley, M. Brown, E. Sanchez, R. S. Tattersall, and J. J. Manson, “COVID-19: consider cytokine storm syndromes and immunosuppression,” Lancet, vol. 395, no. 10229, pp. 1033–1034, 2020.

DOI: http://dx.doi.org/10.24014/ijaidm.v4i2.11990

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