Naïve Bayes Classifier for Predicting the Novel Coronavirus

IEEE BASE PAPER ABSTRACT:

These days, the healthcare enterprises procure huge amount of healthcare data that most of the times is not processed to find out the hidden facts and patterns. Data mining along with machine learning performs a prominent role in predicting the diseases. Nowadays, COVID – 19 has become a pandemic for the mankind. It is a communicable disease and it takes 12 – 24 hours in receiving the reports of diagnose. In various remote and high altitude areas and due to the exponential growth of COVID – 19 in various parts of the world, it is not feasible to perform the test on mass population. This research article describes a novel technique to diagnose coronavirus using naïve bayes classifier and we hope that this technique would be useful and fruitful for the humanity and will be a great step to predict the COVID – 19.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

Random Forest Classifier.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

Sugandh Bhatia, Jyoteesh Malhotra, “Naïve Bayes Classifier for Predicting the Novel Coronavirus”, IEEE Conference, 2021.

Python IEEE Projects

Naïve Bayes Classifier for Predicting the Novel Coronavirus