Abstract
Vector-borne disease is a form of illness which is caused by parasites, viruses and bacteria. The infection is transferred through blood-feeding arthropods such as mosquitoes, fleas, ticks etc. Every year more than 700,000 deaths occur due to diseases such as yellow fever and malaria. These diseases are most common in tropical and subtropical areas and affect the underprivileged populations. Deep learning is an essential part of artificial intelligence that provides an uncanny power to systems to construct a complex network using layers of perceptrons which mimic the human neurons. This network combined with algorithms of machine learning may serve as one of the most powerful tools in healthcare to classify and analyse huge amount of medical data and predict future trends through supervised learning. This paper focused on effective prediction of vector-borne disease outbreak (multiclass classification) of three diseases (chikungunya, malaria, dengue) across the Indian subcontinent. We have examined and refined our model over data collected across India in 2013–2017. We have put forward an artificial neural network outbreak risk prediction algorithm using contrasting data. To our finest understanding, none of the previous works have centred on contrasting data in area of analysis of medical data. The prediction accuracy of our suggested ANN algorithm is 86%.
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Raizada, S., Mala, S., Shankar, A. (2021). Vector-Borne Disease Outbreak Prediction Using Machine Learning Techniques. In: Prakash, K.B., Kannan, R., Alexander, S., Kanagachidambaresan, G.R. (eds) Advanced Deep Learning for Engineers and Scientists. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-66519-7_9
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