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Predictive Analytics for the Coronavirus Recovery Rate in the Philippines

Published: 18 April 2022 Publication History

Abstract

The Philippines is one of the countries where the coronavirus has spread. The virus has infected almost every Filipino individual; coronavirus affects people of all ages, from children to adults, and as a result, recovery rate is unknown. This research aims to develop a predictive model using random forest algorithms to predict the high and low recovery rate by age. Based on the descriptive analysis of the data set, the age range of 20 to 29 has a 99.3 percent recovery rate compared to other age groups. The Random Forest Predictive Model was able to predict the high recovery rate with an accuracy rate of 93%.

References

[1]
CNN Philippines Staff,2020. PH ranks last in COVID-19 recovery report. [online] cnnphilippines.com. https://tinyurl.com/5b6vtcm7
[2]
Cherry Ronai, 2021. COVID-19 Cases in the Philippines. [online] kaggle.com.  https://tinyurl.com/5x2anyd2
[3]
Crisp DM methodology - Smart Vision Europe Crisp DM methodology - Smart Vision Europe, 2021. https://tinyurl.com/yw7bp7m9
[4]
Edrada, E. 2020. "First COVID-19 infections in the Philippines: a case report", Tropical Medicine and Health, 48(1).
[5]
Fox Business, 2020. World Health Organization declares global emergency over virus from China. [online] foxbusiness.com.  https://tinyurl.com/kpedv38h
[6]
Jullie Mc Carthy. 2021. Hospitals in the Philippines struggle under an influx of COVID-19 patients. [online] npr.org. https://www.npr.org/2021/10/02/1042667352/hospitals-in-the-philippines-struggle-under-influx-of-covid-19-patients
[7]
futuredesk.MD, 2021. PH Covid-19 Recoveries Up By 2,591: DOH. [online] mnlmag.com.https://tinyurl.com/2bnrvdtx
[8]
Onesmus Mbaabu, 2020. Introduction to Random Forest in Machine Learning. [online] https://tinyurl.com/bs5be6m7
[9]
Marita Moaje. 2020. DOH reports 90.1% Covid-19 recovery rate. [online] www.pna.gov.ph. https://tinyurl.com/dstudx5d
[10]
Sourav Kumardas. 2020. Random Forest stands apart as it is arguably the most powerful classification model [online] Data Science Central https://www.datasciencecentral.com/random-forest-classification-explained-in-detail-and-developed-in/
[11]
Satyaki Roy and Preetam Gosh, 2020. Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking. [online] scienceopen.com. https://tinyurl.com/uw7f3zrt
[12]
Ma. Teresa Montemayor, 2022. PH logs nearly 98% Covid-19 recovery rate. [online] Pna.gov.ph. https://www.pna.gov.ph/articles/1161491.

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ICSIM '22: Proceedings of the 2022 5th International Conference on Software Engineering and Information Management
January 2022
247 pages
ISBN:9781450395519
DOI:10.1145/3520084
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2022

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Author Tags

  1. CRISP
  2. coronavirus
  3. high and low-level recovery rate
  4. predictive analytics model
  5. random forest algorithm

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