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Prediction Models in Healthcare Using Deep Learning

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Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019) (SoCPaR 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1182))

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Abstract

Predictive models are used to predict the unknown future events using a set of relevant predictors or variables by studying both present and historical data. Predictive modeling is also known as predictive analytics that uses the techniques of statistics, data mining, and artificial intelligence that can be applied to a wide set of applications. A predictive model in healthcare learns the historical data of patients to predict their future conditions and determine the treatment. In this review, the use of deep learning models such as LSTM/Bi-LSTM (Long Short-Term Memory/Bi-directional LSTM), RNN (Recurrent Neural Network), CNN (Convolutional Neural Network), RBM (Restricted Boltzmann Machine) and GRU (Gated Recurrent Unit) on different healthcare applications are highlighted. The results indicate that the LSTM/Bi-LSTM model is widely used in time-series medical data and CNN for medical image data. A deep learning model can assist healthcare professionals to make decisions regarding medications, hospitalizations quickly and thus save time and also serve the healthcare industry better. This paper analyzes the various predictive models used in healthcare applications using deep learning.

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Authors and Affiliations

  1. School of Computing Sciences, Hindustan Institute of Technology and Science, Chennai, Tamil Nadu, India

    S. Bhavya & Anitha S. Pillai

Authors
  1. S. Bhavya
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  2. Anitha S. Pillai
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Corresponding author

Correspondence to S. Bhavya .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR), Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, India

    M. A. Jabbar

  3. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain

    Sanju Tiwari

  4. ISEP - Instituto Superior de Engenharia do Porto, Porto, Portugal

    Isabel M. S. Jesus

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Bhavya, S., Pillai, A.S. (2021). Prediction Models in Healthcare Using Deep Learning. In: Abraham, A., Jabbar, M., Tiwari, S., Jesus, I. (eds) Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019). SoCPaR 2019. Advances in Intelligent Systems and Computing, vol 1182. Springer, Cham. https://doi.org/10.1007/978-3-030-49345-5_21

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