Open Access

ARTICLE

Forecast the Influenza Pandemic Using Machine Learning

by Muhammad Adnan Khan^1,*, Wajhe Ul Husnain Abidi^1,2, 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%.

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