Abstract
A swine flu virus dubbed as H1N1 hit the planet in 2009, impacting millions of people. Swine flu appears to be an infection of the respiratory tract, meaning it damages the respiratory apparatus. This life-threatening virus proved to be a nightmare as it affected millions of people which mainly included children and the middle-aged. It became incredibly important to get vaccinated against this infection as it could save lives. A Couple of vaccines initially proved to be effective in various parts of the world and also among different aged and health conditioned people. In this research, a machine learning model is developed to help in estimating the probability of a person receiving seasonal and H1N1 vaccines. The data for this research is provided by the National 2009 H1N1 Flu Survey (NHFS) team, which conducted a study via telephone calls in the United States. The data consists of a total of 36 attributes. A Gradient boosting classifier is developed to address the problem. The model was evaluated on the separate test data given by the NHFS and the performance metric was the area under Receiver Operating Characteristic (ROC) curve. With the model developed using necessary parameter tuning we were able to achieve the best score of 0.8368.
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Adi, S.P., Bharadwaj, K.V., Bettadapura Adishesha, V. (2021). Predicting Seasonal Vaccines and H1N1 Vaccines Using Machine Learning Techniques. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1441. Springer, Cham. https://doi.org/10.1007/978-3-030-88244-0_1
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