Open AccessSimulation of Real-time Medicine suggestion box for COVID Screening
Author(s) -
Jyothi Vijay,
Damal Kandadai Sriram,
B. Hemanth Sai,
Ramesh Kumar
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2040/1/012015
Subject(s) - covid-19 , field (mathematics) , control (management) , computer science , data science , software , artificial intelligence , disease , medicine , infectious disease (medical specialty) , pathology , mathematics , outbreak , pure mathematics , programming language
Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solutions to control the current havoc can be the diagnosis of disease with the help of various AI tools. The proposed system contains textual data analysis as well as real time physiological data analysis concept. The embedded platform reads the body temperature and heart rate of the patients. The patient is automatically induced to attend the pre-screening survey designed using the software GUI that collects most of the information on symptoms persists. A COVID-19 dataset is collected from publicly available websites. The read survey values and sensor values are pre-processed and extracted the unique features present in it. Those unique parameters are compared with the database to produce the output showing COVID positive status or Negative status and immediate medicine suggestions for them using the global collective medicine suggestions box.