Abstract
The change and evolution of certain health variables can be an evidence that makes easier the diagnosis of infectious diseases. In this kind of diseases, it is important to monitor some patients’ variables along a particular period. It is possible to build a prediction model from registers previously stored with this information. This model can give the probability to develop the disease from input data. Machine learning algorithms can generate these prediction models, which can classify samples composed of clinical parameters in order to predict if an infectious disease will be developed. The prediction models are trained from the patients’ registers previously collected and stored along the time. This work shows an experience of applying machine learning techniques for classifying samples of different infectious diseases. Besides, we have studied the influence on the classification of the different clinical parameters, which could be very useful for the medical staff in order to monitor carefully certain parameters.
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Acknowledgements
This work was funded by the European Union under the project ELAC2015/T09-0819 “Design and Implementation of a Low Cost Smart System for Pre-Diagnosis and Telecare of Infectious Diseases in Elderly People” (SPIDEP) and by the Government of Extremadura (Spain) under the project IB16002.
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Gómez-Pulido, J.A. et al. (2020). Predicting Infectious Diseases by Using Machine Learning Classifiers. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_53
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