Open Access
ARTICLE
Forecast the Influenza Pandemic Using Machine Learning
Muhammad Adnan Khan1,*, Wajhe Ul Husnain Abidi1,2, Mohammed A. Al Ghamdi3, Sultan H. Almotiri3, Shazia Saqib1, Tahir Alyas1, Khalid Masood Khan1, Nasir Mahmood4
1 Department of Computer Science, Lahore Garrison University, Lahore, 54792, Pakistan
2 Systems Limited, Lahore, 54792, Pakistan
3 Computer Science Department, Umm Al-Qura University, Makkah City, 715, Saudi Arabia
4 Department of Computer Science, University of Engineering and Technology, Lahore, 54000, Pakistan
* Corresponding Author: Muhammad Adnan Khan. Email:
(This article belongs to the Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Computers, Materials & Continua 2021, 66(1), 331-340. https://doi.org/10.32604/cmc.2020.012148
Received 16 June 2020; Accepted 24 July 2020; Issue published 30 October 2020
Abstract
Forecasting future outbreaks can help in minimizing their spread. Influenza is a disease primarily found in animals but transferred to humans through
pigs. In 1918, influenza became a pandemic and spread rapidly all over the world
becoming the cause behind killing one-third of the human population and killing
one-fourth of the pig population. Afterwards, that influenza became a pandemic
several times on a local and global levels. In 2009, influenza ‘A’ subtype
H1N1 again took many human lives. The disease spread like in a pandemic
quickly. This paper proposes a forecasting modeling system for the influenza pandemic using a feed-forward propagation neural network (MSDII-FFNN). This
model helps us predict the outbreak, and determines which type of influenza
becomes a pandemic, as well as which geographical area is infected. Data collection for the model is done by using IoT devices. This model is divided into
2 phases: The training phase and the validation phase, both being connected
through the cloud. In the training phase, the model is trained using FFNN and
is updated on the cloud. In the validation phase, whenever the input is submitted
through the IoT devices, the system model is updated through the cloud and predicts the pandemic alert. In our dataset, the data is divided into an 85% training
ratio and a 15% validation ratio. By applying the proposed model to our dataset,
the predicted output precision is 90%.
Keywords
Cite This Article
M. Adnan Khan, W. Ul Husnain Abidi, M. A. Al Ghamdi, S. H. Almotiri, S. Saqib
et al., "Forecast the influenza pandemic using machine learning,"
Computers, Materials & Continua, vol. 66, no.1, pp. 331–340, 2021. https://doi.org/10.32604/cmc.2020.012148
Citations