Predicting COVID-19 disease outcome and post-recovery conditions using machine learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Bibliographic Details
Main Authors:	Sajid, Abul Kasem, Kabir, Fahim, Rahman, Hasibur, Kundu, Indronil, Zaman, Sheersho
Other Authors:	Hossain, Muhammad Iqbal
Format:	Thesis
Language:	English
Published:	Brac University 2021
Subjects:	Symptoms Machine Learning COVID-19 Prediction ICU Emergency Random Forrest KNN COVID-19 (Disease)
Online Access:	http://hdl.handle.net/10361/15433

id	10361-15433
record_format	dspace
spelling	10361-154332022-01-26T10:10:27Z Predicting COVID-19 disease outcome and post-recovery conditions using machine learning Sajid, Abul Kasem Kabir, Fahim Rahman, Hasibur Kundu, Indronil Zaman, Sheersho Hossain, Muhammad Iqbal Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Symptoms Machine Learning COVID-19 Prediction ICU Emergency Random Forrest KNN COVID-19 (Disease) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 46-47). With COVID-19 still running rampant across the world, accurate diagnosis of pa tients and proper management of medical resources is paramount in order to deliver proper care to those that need it most. In order to do this, prediction models with the help of various machine learning algorithms are being developed across the world. Each may deal with certain variables that help predict the disease outcome, such as comorbidities, symptoms, age, sex, etc. Some models have also been made to help predict the chances of a COVID-19 patient in developing lasting medical conditions post recovery. The goal of this research then, is to create a model that takes all the aforementioned dimensions into account and create a prediction model with the three timelines in mind. It is a model that will predict if a person has contacted COVID-19 based on the preliminary symptoms they show (Timeline 1), predict the chances of a COVID-19 patient developing more serious symptoms based on their medical history (Timeline 2) and also predict the chances of a patient developing post-recovery conditions arising after recovering from COVID-19 (Timeline 3). To accomplish this, we use three machine learning algorithms – Random Forest, Na¨ıve Bayes and K-nearest Neighbors. For implementation and testing of the model, data on COVID-19 patients is split into train and test sets and fit over the aforemen tioned algorithms. Their performance are then evaluated. Specific features of the dataset also analyzed at a deeper level in order to gain a better understanding of how the virus behaves in certain conditions. Having such a model in place will not only help us direct medical resources to patients that need the most attention, but will also provide a clearer understanding of the nature of the virus and how it affects a specific patient. Abul Kasem Sajid Fahim Kabir Hasibur Rahman Indronil Kundu Sheersho Zaman B. Computer Science 2021-10-19T06:20:45Z 2021-10-19T06:20:45Z 2021 2021-06 Thesis ID 21141066 ID 17101186 ID 17201024 ID 17201013 ID 21141079 http://hdl.handle.net/10361/15433 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 47 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Symptoms Machine Learning COVID-19 Prediction ICU Emergency Random Forrest KNN COVID-19 (Disease)
spellingShingle	Symptoms Machine Learning COVID-19 Prediction ICU Emergency Random Forrest KNN COVID-19 (Disease) Sajid, Abul Kasem Kabir, Fahim Rahman, Hasibur Kundu, Indronil Zaman, Sheersho Predicting COVID-19 disease outcome and post-recovery conditions using machine learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Hossain, Muhammad Iqbal
author_facet	Hossain, Muhammad Iqbal Sajid, Abul Kasem Kabir, Fahim Rahman, Hasibur Kundu, Indronil Zaman, Sheersho
format	Thesis
author	Sajid, Abul Kasem Kabir, Fahim Rahman, Hasibur Kundu, Indronil Zaman, Sheersho
author_sort	Sajid, Abul Kasem
title	Predicting COVID-19 disease outcome and post-recovery conditions using machine learning
title_short	Predicting COVID-19 disease outcome and post-recovery conditions using machine learning
title_full	Predicting COVID-19 disease outcome and post-recovery conditions using machine learning
title_fullStr	Predicting COVID-19 disease outcome and post-recovery conditions using machine learning
title_full_unstemmed	Predicting COVID-19 disease outcome and post-recovery conditions using machine learning
title_sort	predicting covid-19 disease outcome and post-recovery conditions using machine learning
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15433
work_keys_str_mv	AT sajidabulkasem predictingcovid19diseaseoutcomeandpostrecoveryconditionsusingmachinelearning AT kabirfahim predictingcovid19diseaseoutcomeandpostrecoveryconditionsusingmachinelearning AT rahmanhasibur predictingcovid19diseaseoutcomeandpostrecoveryconditionsusingmachinelearning AT kunduindronil predictingcovid19diseaseoutcomeandpostrecoveryconditionsusingmachinelearning AT zamansheersho predictingcovid19diseaseoutcomeandpostrecoveryconditionsusingmachinelearning
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Predicting COVID-19 disease outcome and post-recovery conditions using machine learning

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