東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Predicting Covid-19 Using Self-Repor...

Vikash Babu, Gokulan.

Linked to FindBook

Google Book

Amazon

博客來

Predicting Covid-19 Using Self-Reported Survey Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predicting Covid-19 Using Self-Reported Survey Data./
Author:	Vikash Babu, Gokulan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	39 p.
Notes:	Source: Masters Abstracts International, Volume: 82-11.
Contained By:	Masters Abstracts International82-11.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28491411
ISBN:	9798738623455

Predicting Covid-19 Using Self-Reported Survey Data.
Vikash Babu, Gokulan.

Predicting Covid-19 Using Self-Reported Survey Data. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 39 p.

Source: Masters Abstracts International, Volume: 82-11.

Thesis (M.S.)--Arizona State University, 2021.

This item must not be sold to any third party vendors.

Infectious diseases spread at a rapid rate, due to the increasing mobility of the human population. It is important to have a variety of containment and assessment strategies to prevent and limit their spread. In the on-going COVID-19 pandemic, telehealth services including daily health surveys are used to study the prevalence and severity of the disease. Daily health surveys can also help to study the progression and fluctuation of symptoms as recalling, tracking, and explaining symptoms to doctors can often be challenging for patients. Data aggregates collected from the daily health surveys can be used to identify the surge of a disease in a community. This thesis enhances a well-known boosting algorithm, XGBoost, to predict COVID-19 from the anonymized self-reported survey responses provided by Carnegie Mellon University (CMU) - Delphi research group in collaboration with Facebook. Despite the tremendous COVID-19 surge in the United States, this survey dataset is highly imbalanced with 84% negative COVID-19 cases and 16% positive cases. It is tedious to learn from an imbalanced dataset, especially when the dataset could also be noisy, as seen commonly in self-reported surveys. This thesis addresses these challenges by enhancing XGBoost with a tunable loss function, α-loss, that interpolates between the exponential loss (α = 1/2), the log-loss (α = 1), and the 0-1 loss (α = ∞). Results show that tuning XGBoost with α-loss can enhance performance over the standard XGBoost with log-loss (α = 1).

ISBN: 9798738623455Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Alpha loss

Predicting Covid-19 Using Self-Reported Survey Data.
LDR:02667nmm a2200385 4500 001 2282069
005 20210927083533.5
008 220723s2021 ||||||||||||||||| ||eng d
020 $a 9798738623455
035 $a (MiAaPQ)AAI28491411
035 $a AAI28491411
040 $a MiAaPQ $c MiAaPQ
100 1 $a Vikash Babu, Gokulan. $3 3560813
245 1 0 $a Predicting Covid-19 Using Self-Reported Survey Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 39 p.
500 $a Source: Masters Abstracts International, Volume: 82-11.
500 $a Advisor: Sankar, Lalitha.
502 $a Thesis (M.S.)--Arizona State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Infectious diseases spread at a rapid rate, due to the increasing mobility of the human population. It is important to have a variety of containment and assessment strategies to prevent and limit their spread. In the on-going COVID-19 pandemic, telehealth services including daily health surveys are used to study the prevalence and severity of the disease. Daily health surveys can also help to study the progression and fluctuation of symptoms as recalling, tracking, and explaining symptoms to doctors can often be challenging for patients. Data aggregates collected from the daily health surveys can be used to identify the surge of a disease in a community. This thesis enhances a well-known boosting algorithm, XGBoost, to predict COVID-19 from the anonymized self-reported survey responses provided by Carnegie Mellon University (CMU) - Delphi research group in collaboration with Facebook. Despite the tremendous COVID-19 surge in the United States, this survey dataset is highly imbalanced with 84% negative COVID-19 cases and 16% positive cases. It is tedious to learn from an imbalanced dataset, especially when the dataset could also be noisy, as seen commonly in self-reported surveys. This thesis addresses these challenges by enhancing XGBoost with a tunable loss function, α-loss, that interpolates between the exponential loss (α = 1/2), the log-loss (α = 1), and the 0-1 loss (α = ∞). Results show that tuning XGBoost with α-loss can enhance performance over the standard XGBoost with log-loss (α = 1).
590 $a School code: 0010.
650 4 $a Computer science. $3 523869
650 4 $a Bioinformatics. $3 553671
650 4 $a Information technology. $3 532993
650 4 $a COVID-19. $3 3554449
653 $a Alpha loss
653 $a Covid risk
653 $a COVID-19 prediction
653 $a Health survey prediction
653 $a COVID-19
653 $a XGBoost algorithm
690 $a 0984
690 $a 0489
690 $a 0715
710 2 $a Arizona State University. $b Computer Science. $3 1676136
773 0 $t Masters Abstracts International $g 82-11.
790 $a 0010
791 $a M.S.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28491411