Prediction of COVID-19 Using the Artificial Neural Network (ANN) with K-Fold Cross-Validation
Journal of Information Systems Engineering and Business Intelligence
; 9(1):16-27, 2023.
Article
in English
| Scopus | ID: covidwho-20232125
ABSTRACT
Background:
COVID-19 is a disease that attacks the respiratory system and is highly contagious, so cases of the spread of COVID-19 are increasing every day. The increase in COVID-19 cases cannot be predicted accurately, resulting in a shortage of services, facilities and medical personnel. This number will always increase if the community is not vigilant and actively reduces the rate of adding confirmed cases. Therefore, public awareness and vigilance need to be increased by presenting information on predictions of confirmed cases, recovered cases, and cases of death of COVID-19 so that it can be used as a reference for the government in taking and establishing a policy to overcome the spread of COVID-19.Objective:
This research predicts COVID-19 in confirmed cases, recovered cases, and death cases in Lampung ProvinceMethod:
This study uses the ANN method to determine the best network architecture for predicting confirmed cases, recovered cases, and deaths from COVID-19 using the k-fold cross-validation method to measure predictive model performance.Results:
The method used has a good predictive ability with an accuracy value of 98.22% for confirmed cases, 98.08% for cured cases, and 99.05% for death cases.Conclusion:
The ANN method with k-fold cross-validation to predict confirmed cases, recovered cases, and COVID-19 deaths in Lampung Province decreased from October 27, 2021, to January 24, 2022. © 2023 The Authors. Published by Universitas Airlangga.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
/
Randomized controlled trials
Language:
English
Journal:
Journal of Information Systems Engineering and Business Intelligence
Year:
2023
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS