Early Detection of COVID-19 Disease Based on Behavioral Parameters and Symptoms Using Algorithm-C5.0
DOI:
https://doi.org/10.24014/ijaidm.v6i1.22074Keywords:
Algorithm C5.0 Eearly Detection Classification Confusion MatrixAbstract
The spread of COVID-19 disease has continued since it was first discovered at the end of 2019 until now. Transmission of COVID-19 is very fast, including through close contact through droplets and through the air. Therefore, early detection of COVID-19 is very important for patients and also those around them to be able to fight the COVID-19 pandemic because if patients get proper and fast treatment, then other people around them will be protected. In this study, an analysis of the classification of decision making for COVID-19 detection was carried out based on behavioral parameters and symptoms that could trigger exposure to COVID-19 using the C5.0 algorithm, followed by measuring the performance of the model using the Confusion Matrix. The C5.0 algorithm is a decision tree-based data mining method. The results of the C5.0 algorithm use a comparison of training data and test data of 70:30. After going through the Confusion Matrix test, an accuracy value of 98% is obtained which indicates that the resulting classification is very good, so that the resulting model can be used for early detection of COVID-19 patients.References
Hafeez, A., Ahmad, S., Siddqui, SA, Ahmad, M., & Mishra, S. (2020). A review of COVID-19 (Coronavirus Disease-2019) diagnosis, treatments and prevention. Ejmo, 4(2), 116-125.
Khishe, M., Caraffini, F., & Kuhn, S. (2021). Evolving deep learning convolutional neural networks for early detection of COVID-19 in chest X-ray images. Mathematics, 9(9), 1002.
Sisodia, D., & Sisodia, DS (2018). Prediction of diabetes using classification algorithms. Procedia computer science, 132, 1578-1585.
Li, JP, Haq, AU, Din, SU, Khan, J., Khan, A., & Saboor, A. (2020). Heart disease identification method using machine learning classification in e-healthcare. IEEE Access, 8, 107562-107582.
Karthikeyan, T., & Thangaraju, P. (2013). Analysis of classification algorithms applied to hepatitis patients. International Journal of Computer Applications, 62(15).
Ramana, BV, Babu, MSP, & Venkateswarlu, NB (2011). A critical study of selected classification algorithms for liver disease diagnosis. International Journal of Database Management Systems, 3(2), 101-114.
Bujlow , T., Riaz, T., & Pedersen, JM (2012, January). A method for classification of network traffic based on C5. 0 Machine Learning Algorithms. In 2012 international conference on computing, networking and communications (ICNC) (pp. 237-241). IEEE.
Pang, SL, & Gong, JZ (2009). C5. 0 classification algorithm and application on individual credit evaluation of banks. Systems Engineering-Theory & Practice, 29(12), 94-104.
Kurniawan, E., Nhita, F., Aditsania, A., & Saepudin, D. (2019, July). C5. 0 algorithm and synthetic minority oversampling technique (SMOTE) for rainfall forecasting in Bandung regency. In 2019 7th International Conference on Information and Communication Technology (ICoICT) (pp. 1-5). IEEE.
Aesyi, US, Diwangkara, TW, & Kurniawan, RT (2020). Diagnosis of Hernia Disk Disease and Spondylolisthesis Using the C5 Algorithm. Telematics: Journal of Informatics and Information Technology, 16(2), 81-86.
https://www.kaggle.com/datasets/hemanthhari/symptoms-and-covid-presence
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., ... & Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet, 395(10223), 497-506.
Hui, DS, Azhar, EI, Madani, TA, Ntoumi, F., Kock, R., Dar, O., ... & Petersen, E. (2020). The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health—The latest 2019 novel coronavirus outbreak in Wuhan, China. International journal of infectious diseases, 91, 264-266.
Mahase, E. (2021). Covid-19: Sore throat, fatigue, and myalgia are more common with the new UK variant.
Iacobucci, G. (2021). Covid-19: Runny nose, headache, and fatigue are the commonest symptoms of omicron, early data show.
COVID-19 National Incident Room Surveillance Team. (2020). COVID-19, Australia: Epidemiology Report 19 (Fortnightly reporting period ending 21 June 2020). Communicable diseases intelligence (2018), 44.
Wilson, RF, Sharma, AJ, Schluechtermann, S., Currie, DW, Mangan, J., Kaplan, B., ... & Gieryn, D. (2020). Factors influencing risk for COVID-19 exposure among young adults aged 18–23 years—Winnebago County, Wisconsin, March–July 2020. Morbidity and Mortality Weekly Report, 69(41), 1497.
