Prediction of COVID 19 using ML
Neelanjali Bapusaheb Chemate, Pratiksha Deepak Pawar, Sakhu Dhondiba Narhe, Shruti Balkrishna Badak, Tejaswini Popat Shinde, Prof. G. B.Yadav · Zenodo (CERN European Organization for Nuclear Research) · 2021
Officially overall the world there is use for multiple breakout prediction/diagnosis models for COVID-19 to take some informed decisions and implement or develop applicant control measures. Simple Clinical &statistical models which have received greater attentiveness from authorities among the standard models for COVID-19 global pandemic prediction, which are very common now a day in Social-media. The Standard models are demonstrating the unsatisfactory accuracy for lengthy Prediction because of very excessive degree of inconsistent and insufficient critical data. While multiple attempts to address this type of problem which are been included in literature are the basic Abstraction and Induction expertness for present models which are needed to be Strengthened. Artificial intelligence incorporation (AI) Wireless infrastructure strategies, real-time compilation, and end-user application processing are now in high demand. Using AI to identify and forecast pandemics of a colossal character is now superlative. The 2019 pandemic of Coronavirus disease (COVID-19), which arose in Wuhan, China. It has had catastrophic consequences on the global economy and has overburdened the world's sophisticated healthcare systems. Globally, according to the European Centre for Disease Prevention and Control Agency, over 4,063,525 confirmed cases and 282,244 deaths were reported as of 11 May 2020.From the present rapid and huge exponential growth in the count of patients, as there is need for an accurate and fast prediction for the potential outcome for infected patient to Accept the care & prediction using techniques for Artificial Intelligence (AI Techniques). This paper proposes an algorithm-boosted, fine-tuned SVM model. This paper proposes an algorithm-boosted, fine-tuned SVM model. The model uses the age of the COVID-19 patient, various symptoms such as dry cough, fever, etc., and the severity of Covid 19 will be compared with the current dataset.