Influenza Prediction: Analyzing Machine Learning AlgorithmsAuthor : Sapna Yadav and Pankaj Agarwal
Volume 9 No.1 January-June 2020 pp 14-18
Analyzing online or digital data for detecting epidemics is one of the hot areas of research and now becomes more relevant during the present outbreak of Covid-19. There are several different types of the influenza virus and moreover they keep evolving constantly in the same manner the COVID-19 virus has done. As a result, they pose a greater challenge when it comes to analyzing them, predicting when, where and at what degree of severity it will outbreak during the flu season across the world. There is need for greater surveillance to both seasonal and pandemic influenza to ensure the health and safety of the mankind. The objective of work is to apply machine learning algorithms for building predictive models that can predict where the occurrence, peak and severity of influenza in each season. For this work we have considered a freely available dataset of Ireland which is recorded for the duration of 2005 to 2016. Specifically, we have tested three ML Algorithms namely Linear Regression, Support Vector Regression and Random Forests. We found Random Forests is giving better predictive results. We also conducted experiment through weka tool and tested Zero R, Linear Regression, Lazy Kstar, Random Forest, REP Tree, Multilayer Perceptron models. We again found the Random Forest is performing better in comparison to all other models. We also evaluated other regression models including Ridge Regression, modified Ridge regression, Lasso Regression, K Neighbor Regression and evaluated the mean absolute errors. We found that modified Ridge regression is producing minimum error. The proposed work is inclined towards finding the suitability & appropriate ML algorithm for solving this problem on Flu.
Epidemics, Influenza Virus, Linear Regression, Support Vector Regression and Random Forests, Zeror, Linear Regression, Lazy Kstar, Random Forest, Reptree
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