Analysing the supervised learning methods for prediction of healthcare data in cloud environment: A Survey

Authors

  • NM Annigeri Dept. of CSE, Ramaiah Institute of Technology, Bengaluru-54, Karnataka, India
  • S Shetty Dept. of CSE, Ramaiah Institute of Technology, Bengaluru-54, Karnataka, India
  • AP Patil Dept. of CSE, Ramaiah Institute of Technology, Bengaluru-54, Karnataka, India

DOI:

https://doi.org/10.26438/ijcse/v6i3.434438

Keywords:

Predictive modeling, predictive algorithms, predictive analytics in a cloud environment, supervised learning

Abstract

In the present era of massive usage of computers, an enormous set of data is being generated from different organizations each day, each hour and each second. This data would be of prodigious use to a diverse set of people based on their needs. Predictive analysis is a process of analysing data and identifying the different patterns in it, so as to predict the occurrence of these patterns in future. The predicted output can help plan a new strategy and adopt innovative solutions for the decision making. This paper attempts to analyse the various predictive models which are applied in the healthcare domain. These models are analysed in depth and will be proposed to be available on the cloud environment in future and can be accessed by those concerned for potential analysis.

References

J. D. Kelleher, B. M. Namee and A. D. Acry, “Fundamentals of machine learning for predictive analytics: algorithms, worked examples and case studies,” MIT Press, 2015.

Siegel, Eric Author. “Predictive Analytics: the Power to Predict Who Will Click, Buy, Lie or Die.” Wiley.

Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J. and Hua, L. (2011). “Data Mining in Healthcare and Biomedicine: A Survey of the Literature. “Journal of Medical Systems, 36(4), pp.2431-2448.

T. Nguyen, A. Khosravi, D. Creighton, and S. Nahavandi, “Classification of healthcare data using genetic fuzzy logic system and wavelets,” Expert Systems with Applications, Elsevier publicaitons, vol. 42, no. 4, pp. 2184–2197, 2015.

T. Nattkemper, B. Arnrich, O. Lichte, W. Timm, A. Degenhard, L. Pointon, C. Hayes and M. Leach, "Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods", Artificial Intelligence in Medicine, Elsevier publication, vol. 34, no. 2, pp. 129-139, 2005.

S. Yu, F. Farooq, A. van Esbroeck, G. Fung, V. Anand and B. Krishnapuram, "Predicting readmission risk with institution-specific prediction models", Artificial Intelligence in Medicine, Elsevier publications, vol. 65, no. 2, pp. 89-96, 2015.

W. Dai, T. Brisimi, W. Adams, T. Mela, V. Saligrama and I. Paschalidis, "Prediction of hospitalization due to heart diseases by supervised learning methods", International Journal of Medical Informatics, vol. 84, no. 3, pp. 189-197, 2015.

G. Toti, R. Vilalta, P. Lindner, B. Lefer, C. Macias and D. Price, "Analysis of correlation between pediatric asthma exacerbation and exposure to pollutant mixtures with association rule mining", Artificial Intelligence in Medicine, Elsevier publication, vol. 74, pp. 44-52, 2016.

D. Delen, G. Walker and A. Kadam, "Predicting breast cancer survivability: a comparison of three data mining methods", Artificial Intelligence in Medicine, vol. 34, no. 2, pp. 113-127, 2005.

L. de Oliveira Silva, A. Barros and M. Lopes, "Detecting masses in dense breast using independent component analysis", Artificial Intelligence in Medicine, vol. 80, pp. 29-38, 2017.

H. Demirkan and D. Delen, "Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud", Decision Support Systems, Elsevier B.V publications, vol. 55, no. 1, pp. 412-421, 2013.

K. Molka and Byrne, James, "Towards Predictive Cost Models for Cloud Ecosystems", poster paper, IEEE Research challenges in Information Science, Paris, 2013.

B. Jin, Y. Wang, Z. Liu and J. Xue, "A Trust Model Based on Cloud Model and Bayesian Networks", Procedia Environmental Sciences, vol. 11, pp. 452-459, 2011.

Anitha H M, P. Jayarekha , "Security Challenges of Virtualization in Cloud Environment", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.37-43, 2018.

Weider D. Yu, Manjula Kollipara, Roopa Penmetsa, Sumalatha Elliadka, "A distributed storage solution for cloud based e-Healthcare Information System", 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013),Lisbon,Portugal 2014.

P Kiran Rao, R Sandeep Kumar, "Machine Learning Methods for Cloud Computing", i-manager’s Journal of Cloud Computing , Vol. 3 No. 4 August - October 2016 .

K.Sree Divya, P.Bhargavi, S.Jyothi “Machine Learning Algorithms in Big data Analytics”, International Journal of Computer Sciences and Engineering, vol.6, issue 1, 2018.

Downloads

Published

2025-11-12
CITATION
DOI: 10.26438/ijcse/v6i3.434438
Published: 2025-11-12

How to Cite

[1]
N. Annigeri, S. Shetty, and A. Patil, “Analysing the supervised learning methods for prediction of healthcare data in cloud environment: A Survey”, Int. J. Comp. Sci. Eng., vol. 6, no. 3, pp. 433–438, Nov. 2025.

Issue

Vol. 6 No. 3 (2018): IJCSE March Edition

Section

Survey Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.