Analysis on dengue severity using machine learning approach

Analysis on dengue severity using machine learning approach

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: Sayeed, Sanjana, Rashid, Iktisad, Sotej, Muktadir Rabbi
Other Authors: Ajwad, Rasif
Format: Thesis
Language:English
Published: Brac University 2021
Subjects:
Dengue
DSS
DHF
Supervised
Unsupervised
Hierarchical clustering
Xg-boosting
Clinical data
Machine Learning
Online Access:http://hdl.handle.net/10361/15734
id 10361-15734
record_format dspace
spelling 10361-157342022-01-26T10:10:22Z Analysis on dengue severity using machine learning approach Sayeed, Sanjana Rashid, Iktisad Sotej, Muktadir Rabbi Ajwad, Rasif Ashraf, Faisal Bin Department of Computer Science and Engineering, Brac University Dengue DSS DHF Supervised Unsupervised Hierarchical clustering Xg-boosting Clinical data 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. Cataloged from PDF version of thesis. Includes bibliographical references (pages 62-65). Dengue is a viral disease that spreads in tropical and subtropical regions and is especially prevalent in South-East Asia. To some certain extent, this mosquito-borne disease triggers nationwide epidemics, which results in large number of fatalities. In our study, we mainly worked with two data sets from two countries: Bangladesh and Vietnam. For the Vietnamese data set we have used supervised learning and implemented different prediction models like Decision Tree Classifier, Random Forest, Gradient Boosting, Ada Boosting, XG-Boosting Classifier Model and have taken the best fitted one (that being XG-Boosting Classifier) to predict the severity amongst the dengue infected patients. After predicting severity we analyzed the data set further to identify what might be the possible cause leading towards the DSS or the DHF for the clinical data. In parallel, for the Bangladeshi data set we applied the unsupervised learning technique, Hierarchical Clustering, to find the different clusters of various vital components of the patients according to their blood report. We then analyzed the clusters further to find the severity among the patients, which led them to DSS or DHF. Sanjana Sayeed Iktisad Rashid Muktadir Rabbi Sotej B. Computer Science 2021-12-13T09:02:59Z 2021-12-13T09:02:59Z 2021 2021-01 Thesis ID 17301189 ID 16231004 ID 16101113 http://hdl.handle.net/10361/15734 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. 65 pages application/pdf Brac University
institution Brac University
collection Institutional Repository
language English
topic Dengue
DSS
DHF
Supervised
Unsupervised
Hierarchical clustering
Xg-boosting
Clinical data
Machine Learning
spellingShingle Dengue
DSS
DHF
Supervised
Unsupervised
Hierarchical clustering
Xg-boosting
Clinical data
Machine Learning
Sayeed, Sanjana
Rashid, Iktisad
Sotej, Muktadir Rabbi
Analysis on dengue severity using machine learning approach
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 Ajwad, Rasif
author_facet Ajwad, Rasif
Sayeed, Sanjana
Rashid, Iktisad
Sotej, Muktadir Rabbi
format Thesis
author Sayeed, Sanjana
Rashid, Iktisad
Sotej, Muktadir Rabbi
author_sort Sayeed, Sanjana
title Analysis on dengue severity using machine learning approach
title_short Analysis on dengue severity using machine learning approach
title_full Analysis on dengue severity using machine learning approach
title_fullStr Analysis on dengue severity using machine learning approach
title_full_unstemmed Analysis on dengue severity using machine learning approach
title_sort analysis on dengue severity using machine learning approach
publisher Brac University
publishDate 2021
url http://hdl.handle.net/10361/15734
work_keys_str_mv AT sayeedsanjana analysisondengueseverityusingmachinelearningapproach
AT rashidiktisad analysisondengueseverityusingmachinelearningapproach
AT sotejmuktadirrabbi analysisondengueseverityusingmachinelearningapproach
_version_ 1814307562390028288

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