АНАЛІЗ МЕТОДІВ ПРОГНОЗУВАННЯ ІНФЕКЦІЙНИХ ЗАХВОРЮВАНЬ
ANALYSIS OF INFECTIOUS DISEASES FORECASTING METHODS
Сторінки: 237–240. Номер: №4, 2022 (311) 
Автори:
СКОПІВСЬКИЙ С.Я.
Національний університет «Львівська політехніка»
e-mail: skopik.stepan@gmail.com
Stepan SKOPIVSKY
Lviv Polytechnic National University
DOI: https://www.doi.org/10.31891/2307-5732-2022-311-4-237-240
Анотація мовою оригіналу
В роботі наведено аналіз різноманітних підходів до прогнозування захворювання на COVID-19. Досліджено сучасні роботи вчених в області поширення інфекційних хвороб, застосування машинного навчання до статистичних даних про розповсюдження COVID, його взаємодії з іншими захворюваннями.
Ключові слова: COVID-19, прогнозування, машинне навчання.
Розширена анотація англійською мовою
At the end of 2019, COVID showed the world its unpreparedness and inability to resist the modification of the influenza virus. The onset of a global pandemic, the spread of the disease, and the large number of deaths have led not only to the search for control of the virus, but also to the possibility of predicting its spread. While some scientists have developed a vaccine, others have studied the prospects of the virus, filling the planet and predicting the number of deaths under certain conditions. Using statistical data, the researchers developed maps of the spread of the virus, possible future targets, and even estimated possible deaths from various strains of COVID-19.
The main task of data forecasting is to create some models from the provided data set in order to provide useful and correct forecasting of future or unknown values of another data set.
For many years, standard statistical methods and mainly the intuition of the doctor, his knowledge and experience have been used to predict the risk of the disease, the occurrence of complications in the patient, the spread of the disease among other people. This approach to disease assessment often leads to unwanted biases, errors and large losses. In the modern field of medicine, such an assessment also has a negative impact on the quality of services provided to patients. With the availability of electronic health data, more reliable and advanced computational approaches have emerged, such as machine learning and big data analysis. The emergence of new methods in data mining has led to the study and application of forecasting methods in the field of disease. In the literature, most relevant studies have used one or more machine learning algorithms to predict a particular disease. For this reason, comparing the effectiveness of different controlled machine learning algorithms for disease prediction is the main focus of this study.
Keywords: COVID-19, forecasting, machine learning.
Література
- Arvind V., Kim J. S., Cho B. H., Geng E., and Cho S. K. Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19. Journal of Critical Care, vol. 62, pp. 25–30, 2021.
- Bi X. et al. Prediction of severe illness due to COVID-19 based on an analysis of initial Fibrinogen to Albumin Ratio and Platelet count. Platelets, vol. 31, no. 5, pp. 674–679, 2020.
- Iwendi C. et al. COVID-19 patient health prediction using boosted random forest algorithm. Frontiers in Public Health, vol. 8, 2020.
- Koppu S., Maddikunt P. K. R. a, and Srivastava G. Deep learning disease prediction model for use with intelligent robots. Computers and Electrical Engineering, vol. 87, 2020.
- Li Q. et al. Cancer increases risk of in-hospital death from COVID-19 in persons <65 years and those not in complete remission. Leukemia, vol. 34, no. 9, pp. 2384–2391, 2020.
- Minaee S., Kafieh R., Sonka M., Yazdani S., and Jamalipour Soufi G. Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. Medical Image Analysis, vol. 65, 2020.
- Shang W. et al. The value of clinical parameters in predicting the severity of COVID-19. Journal of Medical Virology, vol. 92, no. 10, pp. 2188–2192, 2020.
- Tao P.-Y. et al. Determination of risk factors for predicting the onset of symptoms in asymptomatic covid-19 infected patients. International Journal of Medical Sciences, vol. 17, no. 14, pp. 2187–2193, 2020.
- Wang Y. et al. Clinical characteristics and laboratory indicator analysis of 67 COVID-19 pneumonia patients in Suzhou, China. BMC Infectious Diseases, vol. 20, no. 1, 2020.
- Wei W., X.-W. Hu, Q. Cheng, Y.-M. Zhao, and Y.-Q. Ge. Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics. European Radiology, vol. 30, no. 12, pp. 6788–6796, 2020.
- Palaniappan S., Awang R. Intelligent heart disease prediction system using data mining techniques. In: Computer Systems and Applications, 2008. AICCSA 2008. IEEE/ACS International Conference on; 2008. p. 108–15. IEEE.
