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Abstract

Despite a recent wealth of data and information, the healthcare sector is lacking in actionable knowledge. The healthcare industry faces challenges in essential areas like electronic record management, data integration, and computer-aided diagnoses and disease predictions. It is necessary to reduce healthcare costs and the movement towards personalized healthcare. The rapidly expanding fields of deep learning and predictive analytics has started to play a pivotal role in the evolution of large volume of healthcare data practices and research. Deep learning offers a wide range of tools, techniques, and frameworks to address these challenges. Health data predictive analytics is emerging as a transformative tool that can enable more proactive and preventative treatment options. In a nutshell, this paper focus on the framework for deep learning data analysis to clinical decision making depicts the study on various deep learning techniques and tools in practice as well as the applications of deep learning in healthcare.

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Correspondence to Anandhavalli Muniasamy .

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Muniasamy, A., Tabassam, S., Hussain, M.A., Sultana, H., Muniasamy, V., Bhatnagar, R. (2020). Deep Learning for Predictive Analytics in Healthcare. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_4

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