Analisis Faktor Risiko Kematian dengan Penyakit Komorbid COVID-19 menggunakan Algoritma ECLAT
Article Sidebar
Published:
Feb 23, 2022
Main Article Content
Abstract
The death rate due to infection with the COVID-19 virus is increasing. Throughout 2020, COVID-19 cases continued to increase with a total of 2,995,758 positive cases with a total death toll of 204,987 in more than 213 infected countries. The increasing number of deaths is certainly a problem that needs special attention. One of the factors that can affect the severity of COVID-19 infection is a medical condition. These medical conditions are referred to as comorbid or comorbid conditions. This study aims to analyze the risk factors for death of COVID-19 patients based on comorbid diseases using the Data Mining technique. The algorithm used is ECLAT. The results of this study are age and comorbid diseases have an influence on the patient's condition when discharged from the hospital with a support value of 25% and a confidence value of 100%.
Article Details
How to Cite
Evadini, S. (2022). Analisis Faktor Risiko Kematian dengan Penyakit Komorbid COVID-19 menggunakan Algoritma ECLAT. Jurnal Informasi Dan Teknologi, 4(1), 52-57. https://doi.org/10.37034/jidt.v4i1.181
Issue
Section
Articles
References
[1] Sutaryono, S., Andasari, S. D., & Kasjono, H. S. (2020). Diagnosis and epidemiology of Coronavirus (COVID-19) outbreak in Indonesia. Jurnal Teknologi Laboratorium, 9(1), 49–57. doi:10.29238/teknolabjournal.v9i1.222
[2] Li, X., Zhong, X., Wang, Y., Zeng, X., Luo, T., & Liu, Q. (2021). Clinical determinants of the severity of COVID-19: A systematic review and meta-analysis. PLOS ONE, 16(5), e0250602. doi:10.1371/journal.pone.0250602
[3] Wang, X., Fang, X., Cai, Z., Wu, X., Gao, X., Min, J., & Wang, F. (2020). Comorbid Chronic Diseases and Acute Organ Injuries Are Strongly Correlated with Disease Severity and Mortality among COVID-19 Patients: A Systemic Review and Meta-Analysis. Research, 2020, 1–17. doi:10.34133/2020/2402961
[4] Guo, L., Shi, Z., Zhang, Y., Wang, C., Do Vale Moreira, N. C., Zuo, H., & Hussain, A. (2020). Comorbid diabetes and the risk of disease severity or death among 8807 COVID-19 patients in China: A meta-analysis. Diabetes Research and Clinical Practice, 166, 108346. doi:10.1016/j.diabres.2020.108346
[5] Ritchie, H., Mathieu, E., Rodés-Guirao, L., Appel, C., Giattino, C., Ortiz-Ospina, E., Hasell, J., Macdonald, B., Beltekian, D., & Roser, M. (2020). Coronavirus Pandemic (COVID-19). Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/coronavirus'
[6] Sanchez Sanchez, P. A., Cano Zuluaga, J., Garcia Herazo, D., Pinzon Baldion, A. F., Rodriguez Mercado, G., Garcia Gonzalez, J. R., & Perez Coronell, L. H. (2019). Knowledge Discovery in Musical Databases for Moods Detection. IEEE Latin America Transactions, 17(12), 2061–2068. doi:10.1109/tla.2019.9011552
[7] Yu, K., Yuan, T., & Li, Y. (2021). Application of Data Mining Technology in Sports Data Analysis in Colleges and Universities. 2021 International Conference on Information Technology and Contemporary Sports (TCS). doi:10.1109/tcs52929.2021.00073
[8] Solanki, S. K., & Patel, J. T. (2015). A Survey on Association Rule Mining. 2015 Fifth International Conference on Advanced Computing & Communication Technologies. doi:10.1109/acct.2015.69
[9] Mohapatra, D., Tripathy, J., Mohanty, K. K., & Nayak, D. S. K. (2021). Interpretation of Optimized Hyper Parameters in Associative Rule Learning using Eclat and Apriori. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). doi:10.1109/iccmc51019.2021.9418049
[10] Robu, V., & dos Santos, V. D. (2019). Mining Frequent Patterns in Data Using Apriori and Eclat: A Comparison of the Algorithm Performance and Association Rule Generation. 2019 6th International Conference on Systems and Informatics (ICSAI). doi:10.1109/icsai48974.2019.9010367
[11] Zhao, Y., Lv, Y., Zeng, J., Dong, Y., Fang, H., Yu, P., & Xu, S. (2021). Mining fault association rules in the perception layer of electric power sensor network based on improved Eclat. 2021 International Wireless Communications and Mobile Computing (IWCMC). doi:10.1109/iwcmc51323.2021.9498688
[12] Bao, G., Mei, Y., Li, G., & Wang, G. (2021). Analysis of Students Behavior Characteristics Based on K-mediods + Eclat. 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). doi:10.1109/cscwd49262.2021.9437638
[13] Percin, I., Yagin, F. H., Guldogan, E., & Yologlu, S. (2019). ARM: An Interactive Web Software for Association Rules Mining and an Application in Medicine. 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). doi:10.1109/idap.2019.8875885
[14] Zhang, C., Tian, P., Zhang, X., Jiang, Z. L., Yao, L., & Wang, X. (2019). Fast Eclat Algorithms Based on Minwise Hashing for Large Scale Transactions. IEEE Internet of Things Journal, 6(2), 3948–3961. doi:10.1109/jiot.2018.2
[2] Li, X., Zhong, X., Wang, Y., Zeng, X., Luo, T., & Liu, Q. (2021). Clinical determinants of the severity of COVID-19: A systematic review and meta-analysis. PLOS ONE, 16(5), e0250602. doi:10.1371/journal.pone.0250602
[3] Wang, X., Fang, X., Cai, Z., Wu, X., Gao, X., Min, J., & Wang, F. (2020). Comorbid Chronic Diseases and Acute Organ Injuries Are Strongly Correlated with Disease Severity and Mortality among COVID-19 Patients: A Systemic Review and Meta-Analysis. Research, 2020, 1–17. doi:10.34133/2020/2402961
[4] Guo, L., Shi, Z., Zhang, Y., Wang, C., Do Vale Moreira, N. C., Zuo, H., & Hussain, A. (2020). Comorbid diabetes and the risk of disease severity or death among 8807 COVID-19 patients in China: A meta-analysis. Diabetes Research and Clinical Practice, 166, 108346. doi:10.1016/j.diabres.2020.108346
[5] Ritchie, H., Mathieu, E., Rodés-Guirao, L., Appel, C., Giattino, C., Ortiz-Ospina, E., Hasell, J., Macdonald, B., Beltekian, D., & Roser, M. (2020). Coronavirus Pandemic (COVID-19). Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/coronavirus'
[6] Sanchez Sanchez, P. A., Cano Zuluaga, J., Garcia Herazo, D., Pinzon Baldion, A. F., Rodriguez Mercado, G., Garcia Gonzalez, J. R., & Perez Coronell, L. H. (2019). Knowledge Discovery in Musical Databases for Moods Detection. IEEE Latin America Transactions, 17(12), 2061–2068. doi:10.1109/tla.2019.9011552
[7] Yu, K., Yuan, T., & Li, Y. (2021). Application of Data Mining Technology in Sports Data Analysis in Colleges and Universities. 2021 International Conference on Information Technology and Contemporary Sports (TCS). doi:10.1109/tcs52929.2021.00073
[8] Solanki, S. K., & Patel, J. T. (2015). A Survey on Association Rule Mining. 2015 Fifth International Conference on Advanced Computing & Communication Technologies. doi:10.1109/acct.2015.69
[9] Mohapatra, D., Tripathy, J., Mohanty, K. K., & Nayak, D. S. K. (2021). Interpretation of Optimized Hyper Parameters in Associative Rule Learning using Eclat and Apriori. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). doi:10.1109/iccmc51019.2021.9418049
[10] Robu, V., & dos Santos, V. D. (2019). Mining Frequent Patterns in Data Using Apriori and Eclat: A Comparison of the Algorithm Performance and Association Rule Generation. 2019 6th International Conference on Systems and Informatics (ICSAI). doi:10.1109/icsai48974.2019.9010367
[11] Zhao, Y., Lv, Y., Zeng, J., Dong, Y., Fang, H., Yu, P., & Xu, S. (2021). Mining fault association rules in the perception layer of electric power sensor network based on improved Eclat. 2021 International Wireless Communications and Mobile Computing (IWCMC). doi:10.1109/iwcmc51323.2021.9498688
[12] Bao, G., Mei, Y., Li, G., & Wang, G. (2021). Analysis of Students Behavior Characteristics Based on K-mediods + Eclat. 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). doi:10.1109/cscwd49262.2021.9437638
[13] Percin, I., Yagin, F. H., Guldogan, E., & Yologlu, S. (2019). ARM: An Interactive Web Software for Association Rules Mining and an Application in Medicine. 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). doi:10.1109/idap.2019.8875885
[14] Zhang, C., Tian, P., Zhang, X., Jiang, Z. L., Yao, L., & Wang, X. (2019). Fast Eclat Algorithms Based on Minwise Hashing for Large Scale Transactions. IEEE Internet of Things Journal, 6(2), 3948–3961. doi:10.1109/jiot.2018.2