Online ISSN: 2515-8260

Keywords : Decision Trees

Asymptomatic C ommunity Spread Of Coronavirus Disease 2019( COVID 19) Outbreak Prediction Using Logistic Regression

Robin Singh Bhadoria; Neha Sharma; Manish Kumar Pandey; Bishwajeet Pandey

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 6, Pages 2849-2863

Corona virus disease ( COVID 19 pandemic has become a major threat to the
entire world. Antidotes and proper medication s are still not found and determined to get
cure from such virus . The report from World Health Organization ( WHO ) remits the
COVID 19 as severe acute respiratory syndrome (SARS). Such virus is transmitted into
human body via a respiratory droplets. Even, major symptoms fo r coronavirus patience are
tiredness, severe fever and dry cough but in most of the cases such symtoms are not
found. This variety of coronavirus symptoms are termed as asymptomatic sym p toms. The
identification for such disease is very important into hum an body so that this can be
stopped as community spread and reduces the effect of this as global pandemic. T his paper
provides an extensive study and predicts the outbreak of this disease with the aid of
classification techniques of under machine learning. So that, the number of cases related
to COVID 19 can be identified and subsequent arrangements have been made from the
respective governments and medical doctors for future . Initially, th is prediction model is
implemented for short term interval and later, such model based on internet of thing and
machine learning, can also be set for estimating into long term intervals for global as well
as Indian perspective. The logistic r egression and d ecisiont ree techniques have been used
for such cases predictions for this epidemic