Artificial Intelligence and Machine Learning for Health Risks Prediction

Luke Oluwaseye Joel, Wesley Doorsamy, Babu Sena Paul

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


The emergence of the fourth industrial revolution has brought about the drive towards integrating technologies, such as machine learning, into healthcare solutions. This chapter explores how applications of machine learning in the healthcare sector have sought progress, and extricate the challenges with respect to early prediction of chronic illnesses. The review of past work in this fast-growing research and development area, as well as the state-of-the-art, is conducted in order to establish a roadmap for future trajectories. This study emphasises that there is no ML algorithm that can guarantee a reliable predictive outcome for all kinds of diseases in every given problem case, the quantity of the dataset employed has a significant contribution to the performance of the predictive algorithms, and that quality time must be given to the data preparation stages because the result of the ML algorithms employed depends on the quality of the dataset used. This work is mainly intended to serve researchers and developers in the field. The readership will also extend to practitioners and policy-makers.

Original languageEnglish
Title of host publicationStudies in Fuzziness and Soft Computing
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages23
Publication statusPublished - 2021

Publication series

NameStudies in Fuzziness and Soft Computing
ISSN (Print)1434-9922
ISSN (Electronic)1860-0808


  • COVID-19
  • Disease
  • Health risk
  • Machine learning
  • Prediction

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mathematics


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