A State-of-the-Art Artificial intelligence model for Infectious Disease Outbreak Prediction. Infectious disease outbreak have been predicted in England and Wales using Artificial Intelligence, Machine learning, and Fast Fourier Transform for COVID-19.

The pandemic produced by the COVID-19 virus has resulted in an estimated 6.4 million deaths worldwide and a rise in unemployment rates, notably in the UK. Healthcare monitoring systems encounter several obstacles when regulating and anticipating epidemics. The study aims to present the AF-HIDOP model, an artificial neural network Fast Fourier Transform hybrid technique, for the early identification and prediction of the risk of Covid-19 spreading within a specific time and region. The model consists of the following five stages: 1) Data collection and preprocessing from reliable sources; 2) Optimal machine learning algorithm selection, with the Random Forest tree (RF) classifier achieving 94.4% accuracy; 3) Dimensionality reduction utilising principal components analysis (PCA) to optimise the impact of the data volume; 4) Predicting case numbers utilising an artificial neural network model, with 52% accuracy; 5) Enhancing accuracy by incorporating Fast Fourier Transform (FFT) feature extraction and ANN, resulting in 91% accuracy for multi-level spread risk classification. The AF-HIDOP model provides prediction accuracy ranging from moderate to high, addressing issues in healthcare-based datasets and costs of computing, and may have potential uses in monitoring and managing infectious disease epidemics.

Grantor dc:publisher.institution

University of Bradford

Year dc:date.issued

2023

Author dc:creator

• Fayad, Moataz B.M.

Advisors dc:contributor.advisor

• Youseffi, Mansour
• Li, Jian-Ping

• Covid-19 outbreak
• Prediction
• Artificial Intelligence (AI)
• Machine learning
• Fast Fourier Transform
• Computational cost
• Infectious disease outbreak
• Epidemics

Statement dc:rights

• <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10454/19906

OAI identifier oai:identifier

oai:bradscholars.brad.ac.uk:10454/19906