Deep Learning for Predictive Analytics in Healthcare

  • Anandhavalli MuniasamyEmail author
  • Sehrish Tabassam
  • Mohammad A. Hussain
  • Habeeba Sultana
  • Vasanthi Muniasamy
  • Roheet Bhatnagar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)


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.


Healthcare data Electronic medical records Deep learning (DL) Predictive analytics 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Anandhavalli Muniasamy
    • 1
    Email author
  • Sehrish Tabassam
    • 1
  • Mohammad A. Hussain
    • 1
  • Habeeba Sultana
    • 1
  • Vasanthi Muniasamy
    • 1
  • Roheet Bhatnagar
    • 2
  1. 1.College of Computer ScienceKing Khalid UniversityAbhaKingdom of Saudi Arabia
  2. 2.Department of Computer Science and EngineeringManipal University JaipurRajasthanIndia

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