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http://dx.doi.org/10.5657/KFAS.2020.0740

Data Mining for Scuticociliatosis Outbreak Patterns in Cultured Olive Flounder Paralichthys olivaceus in Jeju, Korea  

Kim, Hae-Ran (Department of Digital Convergence, Chonnam National University)
Jung, Sung-Ju (Department of Aqualife Medicine, Chonnam National University)
Kim, Sung-Hyun (Fishcare Laboratory)
Park, Jeong-Seon (Department of Multimedia, Chonnam National University)
Ceong, Hee-Taek (Department of Multimedia, Chonnam National University)
Han, Soon-Hee (Department of Multimedia, Chonnam National University)
Publication Information
Korean Journal of Fisheries and Aquatic Sciences / v.53, no.5, 2020 , pp. 740-751 More about this Journal
Abstract
In the aquaculture industry, few studies are analyzing big data for intrinsic meaning. Fishcare Laboratory (www.fishcare.kr) diagnostic data from 2016-2018 was analyzed for scuticociliatosis (caused by Miamiensis avidus) outbreak patterns in cultured olive flounder Paralichthys olivaceus in Jeju, Korea. The scuticociliatosis monthly occurrence ratio is reported in the summary table after preparing and filtering the basic dataset model. Nonparametric test results suggest differences in the water temperature, body length, and weight between groups with and without scuticociliatosis. Data distribution visualization revealed that shorter body length and lighter weight increased the occurrence of scuticociliatosis. The association rule mining technique was applied to determine the primary clinical signs of mixed scuticociliatosis and bacterial infections. Venn diagrams were used to report clinical signs and suggest commonalities. These results may help diagnose and treat fish and provide a decision-making reference.
Keywords
Olive flounder; Paralichthys olivaceus; Scuticociliatosis; Association rule; Data mining;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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1 Jin CN, Kang HS, Lee CH, Lee YD, Lee JH and Heo MS. 2007. Biological characteristics of Scuticociliate Philasterides dicentrarchi isolated from cultured olive flounder, Paralichthys olivaceus. J Aquaculture 20, 106-113.
2 Jin CN, Lee CH, Oh SP, Jung YU, Song CB, Lee J and Heo MS. 2003. Scuticociliatosis in flounder farms of Jeju island. J Fish Pathol 16, 135-138.
3 Kang BJ. 2019. Machine learning directly in field with Python. Acorn press, Seoul, Korea, 377-388.
4 Kim JW, Cho MY, Park GH, Won KM, Choi HS, Kim MS and Park MA. 2010. Statistical data on infectious diseases of cultured olive flounder Paralichthys olivaceus from 2005 to 2007. J Fish Pathol 23, 369-377.
5 Kim JW, Jung SH, Park MA, Do JW, Choi DL, Jee BY, Cho MY, Kim MS, Choi HS, Kim YC, Lee JS, Lee CH, Bang JD, Park MS and Seo JS. 2006. Monitoring of Pathogens in Cultured Fish of Korea for the Summer Period from 2000 to 2006. J Fish Pathol 19, 207-214.
6 Kim SH. 2018. Companion disease analysis of patients with chronic obstructive pulmonary disease using a sequencial association rule. In: Report of Health Insurance Review Assessment. HIRA Report 12, 53-59.
7 Lee IH, Shin AM, Son CS, Park HJ, Kim JH, Park SY, Choi JH and Kim YN. 2010. Association analysis of comorbidity of cerebral infarction using data mining. J Kor Soc Phys Ther 22, 75-81.   DOI
8 Park HS, Lee MS, Hwang SJ and Oh SY. 2016. TF-IDF based association rule analysis system for medical data. KIPS Tr. Software Data Eng 5, 145-154. https://doi.org/10.3745/KTSDE.2016.5.3.145.   DOI
9 Rfriend. 2015. R analysis and programming. Retrieved from https://rfriend.tistory.com/ on May 7, 2020.
10 Sabthami J, Thirumoorthy K and Muneeswaran K. 2016. Mining association rules for early diagnosis of diseases from electronic health records. Middle East J Sci Res 24, 248-253. https://doi.org/10.5829/idosi.mejsr.2016.24.S2.159.
11 Sebastiao FA, Furlan LR, Hashimoto DT and Pilarski F. 2015. Identification of bacterial fish pathogens in Brazil by direct colony PCR and 16s rRNA gene sequencing. Adv Microbiol 5, 409-424. https://doi.org/10.4236/aim.2015.56042.   DOI
12 Tai YM and Chiu HW. 2009. Comorbidity study of ADHD: Applying association rule mining (ARM) to National Health Insurance Database of Taiwan. Int J Med Inform 78, e75-e83. https://doi.org/10.1016/j.ijmedinf.2009.09.005.   DOI
13 Cho MY, Kim MS, Choi HS, Park GH, Kim JW, Park MS and Park MA. 2008. A statistical study on infectious diseases of cultured olive flounder Paralichthys olivaceus in Korea. J Fish Pathol 21, 271-278.
14 Bae MJ, Im EY, Kim HY and Jung SJ. 2009. The effect of temperature to scuticociliatida Miamiensis avidus proliferation, and to mortality of infected olive flounder Paralichthys olivaceus. J Fish Pathol 22, 97-105.
15 Choi JH, Lee IH, Kim JH, Park SY, Shin AM, Son CS, Park HJ and Kim YN. 2010. Association analysis of comorbidity of cerebral infarction using data mining. J Kor Soc Phys Ther 22, 75-81.   DOI
16 Cho MY, Kim KI, Min EY and Jung SH. 2019. Global outbreaks and strategies to control the emerging diseases in aquaculture farms in Korea. Ocean Policy Res 34, 67-88. https://doi.org/10.35372/kmiopr.2019.34.1.003.   DOI
17 Han SR, Han HS, Evensen O and Kim SH. 2017. PCR-based identification of Pseudomonas fluorescens in diseased olive flounder Paralichthys olivaceus, in Jeju Island, South Korea. J Fish Pathol 30, 67-70. https://doi.org/10.7847/jfp.2017.30.1.067.   DOI
18 Jin CN, Kang BJ, Jang YH, Park BH and Jhon BK. 2015. Monitoring of scuticociliatosis of olive flounder Paralichthys olivaceus farm in Jeju, Korea from 2007 to 2014. J Fish Pathol 28, 165-169. https://doi.org/10.7847/jfp.2015.28.3.165.   DOI