Knowledge-based approach for predicting covid-19 using Machine learning
Author(s):
Subradev Sarkar, Suva Ghosh, Tanmoy Paul, Sourav Das, Dharmpal Singh, Sudipta Sahana
Keywords:
Covid-19, Healthcare, Machine Learning, Factor analysis, Clustering, K-means, Hierarchical, Knowledge-based.
Abstract
For the last two years, the entire world is suffering from covid-19 diseases. It declares a global pandemic by the WHO. At the earlier stages, Indian people were not aware of this virus and now the cases are increasing. The technique proposed in this paper is an efficient solution for identifying the covid cases and will help us to know whether a person is covid positive or not without medical testing. The technology behind this project is Machine Learning. Which is an advanced and efficient solution to make predictions easy and give a perfect result. In this project, our effort is that based on the symptoms of the covid-19 we can predict whether a person is suffering from covid-19 or not. This method is possible by using patients' health reports where it has been recorded that, which patent is covid positive and which one is negative and can help create knowledge-based by using Machine learning. So, after collecting the data first data-cleaning processes have been done. Then after data-cleaning, correlations have been found to identify the most important features by using the Factor analysis technique. From Factor analysis, the Total effect value has been collected, then the Total effect value has been divided into two clusters 0 and 1. After that based on the cluster-centers a knowledge-based have been created, From where it can be determined that if the total effect value of the new test data is in the range of any cluster value, it will belong to that particular cluster and can be easily said that if it belongs to 0 clusters the result will be negative or if it belongs to 1 cluster the result will be positive.
Article Details
Unique Paper ID: 151878

Publication Volume & Issue: Volume 8, Issue 1

Page(s): 1049 - 1054
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