Identification of parameters for classification of COVID-19 patient’s recovery days using machine learning techniques

Digambar Uphade, Aniket Muley

Abstract


Nowadays, Corona virus has been spreading all over the world. Discovery of various perspectives is going on. Our aim is to identify the recovery days of patients from the Covid-19 disease. To classify the patient using various parameters that affect his/her recovery days. It is complex to deal with numerous parameters, so to reduce the complexity feature selection techniques were employed. In this study, we have dealt with different machine learning approaches for classifying the patients dataset collected through the online survey methodology. We are pioneers in dealing with aspects. Based on these techniques, our interest is to classify the patients as based on the number of recovery days. This present study has major contributions as a method of classification and is an easily understandable way using statistical visualization plots viz., bar plots, pie charts etc. The machine learning algorithms like Logistic regression, Decision tree, Random forest, Neural network, Support vector machine, K Nearest Neighbor were used for performing this task. Further, comparative study is performed and the neural network gives better accuracy to classify the respondents. Finally, results explored with supervised learning are more accurate to detect the COVID-19 recovery patients’ cases and neural network is found to be an efficient algorithm as compared with other algorithms (100%).

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Published: 2022-01-24

How to Cite this Article:

Digambar Uphade, Aniket Muley, Identification of parameters for classification of COVID-19 patient’s recovery days using machine learning techniques, J. Math. Comput. Sci., 12 (2022), Article ID 56

Copyright © 2022 Digambar Uphade, Aniket Muley. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

 

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