Authors:
Yosra Didi
1
;
Ahlem Walha
1
and
Ali Wali
2
Affiliations:
1
Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia, Research Laboratory on Intelligent Machines, University of Sfax, National Engineering School of Sfax, BP1173 Sfax 3038, Tunisia
;
2
Research Laboratory on Intelligent Machines, University of Sfax, National Engineering School of Sfax, BP1173 Sfax 3038, Tunisia
Keyword(s):
Prediction, Illness Like Influenza (ILI), LSTM, Machine Learning, Time Series Forecasting, Climatic Changes, Air Pollution.
Abstract:
Influenza is one of the most severe and prevalent epidemic that causes mortality and morbidity. The researcher focused on early forecasting to prevent and control the outbreak of the flu disease, which it may reduce their impact on our daily lives. We propose a model based on machine learning methods that is capable of making timely influenza prediction using the impact of many environmental factors such as climatic variables, air pollutants and geographical proximity. Our significant contribution is to incorporate the impact of this environmental factors changes on the spread of the disease with a machine learning method to improve the performance of the influenza prediction models. We use multiple data sources including Illness Like Influenza (ILI) data, climatic factors, air pollutant and geographic proximity that have significant correlation with ILI rate. In this paper, we compare the proposed model with two methods and with the actual value to prove the effectiveness of our app
roach.
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