Analytics of Epidemiological Data using Machine Learning Models
##plugins.themes.academic_pro.article.sidebar##
##plugins.themes.academic_pro.article.main##
Abstract
Epidemiological data is the data obtained based on disease, injury or environmental hazard occurrence using the previous data on the epidemic situation. We can use it for analysis and find the trends and patterns. We can use different machine learning models to create a platform that can be used for different time series data. We can rely on the properties of time series data like trends and seasonality and use this for future prediction. Acquiring the dataset is the first step in data preprocessing in machine learning. We have collected the dataset from ourWorldIndia website which is a real-life dataset of covid-19. This paper presents the idea of a dedicated machine learning model to forecast the future using epidemiological data. We have taken a data-set of covid-19 for the prediction of the number of daily cases infected by the coronavirus. Our machine learning model can be applied on the dataset of any country in the world. We have applied it on the dataset of India in the experimentation. Our goal behind this research paper is to give the ML model which can be easily used on any epidemiological data for prediction by analysing the seasonality.
##plugins.themes.academic_pro.article.details##
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
- CHINTALAPUDI, N., B. G. and AMENTA, F. 2020. Covid-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in italy: A data driven model approach. Journal of Microbiology, Immunology and Infection Vol.53, pp.396–403. DOI: https://doi.org/10.1016/j.jmii.2020.04.004
- George E. Halkos, I. S. K. 2021. Testing for a unit root under the alternative hypothesis of arima (0,2,1). applied economics. International Journal of Next-Generation Computing Vol.21, pp.2753–2767. DOI: https://doi.org/10.1080/00036840600735416
- KUMAR, N. and SUSAN. 2020. Covid-19 pandemic prediction using time series forecasting models. pp.1–7. DOI: https://doi.org/10.1109/ICCCNT49239.2020.9225319
- MAHALLE, P. N. and SHINDE, G. R. 2020. Data analytics: Covid-19 prediction using multimodal data. Springer Singapore, Singapore. DOI: https://doi.org/10.20944/preprints202004.0257.v2
- MURRAY and J, C. 2020. Forecasting covid-19 impact on hospital bed-days, icu-days, ventilator- days and deaths by us state in the next 4 months. medRxiv . Vol.14.
- R Sankalp Shukla, Prof. G M Walunjkar, D. R. D. Y. G. 2022. Prediction of market trends using machine learning. International Journal of Next-Generation Computing (IJNGC) Vol.13, No.2. DOI: https://doi.org/10.47164/ijngc.v13i2.575
- RIBEIRO, M. H. D. M., D. S. R. G. M. 2020. Short-term forecasting covid-19 cumulative confirmed cases: Perspectives for brazil. Chaos, Solitons Fractals Vol.135, pp.396–403. DOI: https://doi.org/10.1016/j.chaos.2020.109853
- Sakshi Saklani, Ashish Chandak, P. J. A. A. R. . A. L. 2022. A covid outbreak prediction using machine learning. International Journal of Next-Generation Computing Vol.13, pp.396–403. DOI: https://doi.org/10.47164/ijngc.v13i5.925
- Satyajit Uparkar, Dhote, S. P. S. S. P. . D. D. 2022. Comparative analysis of scalability approaches using data mining methods on health care datasets. International Journal of Next-Generation Computing Vol.13, pp.396–403. DOI: https://doi.org/10.47164/ijngc.v13i5.960
- SHARMA and RAY. Evdhm-arima-based time series forecasting model . IEEE Transaction.
- Siemuri, A., A. R. O. and Elmusrati, M. 2020. Covid-19: Easing the coronavirus lockdowns with caution. medRxiv. Vol.21, pp.2753–2767.