Abstract
The healthcare sector generates around one trillion Gigabytes of clinical data annually. With limited resources, manually analyzing these massive amounts of data is tremendously time-consuming. Latest advancements in Deep Learning (DL) have been shown as an efficacious approach to building end-to-end learning models for disease prognosis and diagnosis. In the past, discovering information from data has been accomplished through conventional machine learning techniques. Problems with these techniques are that they do not scale appropriately with the increase in data due to a lack of domain knowledge. This work briefly explained popular algorithms based on the state-of-the-art related to DL and the healthcare sector. These algorithms can potentially prevent infectious diseases, reducing operating costs and efforts. Finally, significance and importance of DL in healthcare are discussed to aid readers in formulating new healthcare research problems.
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Aman, Chhillar, R.S. (2023). The Upsurge of Deep Learning for Disease Prediction in Healthcare. In: Bhattacharya, A., Dutta, S., Dutta, P., Piuri, V. (eds) Innovations in Data Analytics. ICIDA 2022. Advances in Intelligent Systems and Computing, vol 1442. Springer, Singapore. https://doi.org/10.1007/978-981-99-0550-8_40
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