Smart Health: Advancements in Machine Learning and the Internet of Things Solutions

Research summary

The summary, key findings, methodology and relevance notes below are this website’s own description of the chapter, written from the published abstract and text. The official abstract and citation details are given further down.

The Problem

Machine learning and the Internet of Things promise better healthcare, but their integration with healthcare analytics raises unresolved technical, ethical and security problems.

The Methodology

Review chapter on the convergence of healthcare analytics, machine learning and IoT: benefits, integration challenges and recommended solutions, ethical and security considerations, and emerging advances.

The Core Finding

The chapter shows that combining machine learning and IoT supports enhanced patient care, early disease detection, operational efficiency and personalised treatment, and that data privacy, model interpretability, bias mitigation and secure connectivity are prerequisites for deployment.

The Citation

Vajjhala, N. R., & Eappen, P. (2024). Smart Health: Advancements in Machine Learning and the Internet of Things Solutions. In Pijush Samui, Sanjiban Sekhar Roy, Wengang Zhang, Y-h. Taguchi (Eds.), Machine Learning and IoT Applications for Health Informatics (pp. 31–51). CRC Press. https://doi.org/10.1201/9781003424987-3 Copy APA

What question does this chapter answer?

How do machine learning and the Internet of Things, working together with healthcare analytics, reshape healthcare delivery, and what technical, ethical and security challenges must be addressed to deploy them?

What did the chapter find, in detail?

The chapter examines the intersection of healthcare analytics, machine learning and IoT, focusing on benefits such as enhanced patient care, early disease detection, operational efficiency and personalised treatment plans. It sets out the problems and challenges of integrating ML and IoT with healthcare analytics and recommends solutions, addressing ethical, practical and security considerations — data privacy, model interpretability, bias mitigation and secure connectivity — and presents ongoing technological advances with an emphasis on patient-centric approaches and global health disparities.

Why does it matter?

ML and IoT are being deployed in healthcare faster than the governance around them matures. The chapter combines the technical promise with the practical and ethical conditions for safe, equitable deployment.

Chapter at a glance

Abstract

This chapter examines the transformative intersection of healthcare analytics, Machine Learning (ML), and the Internet of Things (IoT), exploring how these state-of-the-art technologies reshape healthcare delivery. This chapter focuses on the benefits brought about by the synergy of ML and IoT in the healthcare sector, such as enhanced patient care, early disease detection, operational efficiency, and personalized treatment plans. We explore the problems and challenges of integrating ML and IoT with healthcare analytics and recommend solutions in this chapter. The chapter also addresses ethical, practical, and security considerations, emphasizing the importance of data privacy, model interpretability, bias mitigation, and secure connectivity in deploying healthcare technology. Furthermore, this chapter also presents the ongoing technological advancements and their potential to augment healthcare analytics further, emphasizing the need for patient-centric approaches and addressing global health disparities. This chapter explores healthcare analytics’ current landscape and prospects, considering the integration of ML and IoT solutions.

Abstract as published in Machine Learning and IoT Applications for Health Informatics.

Limitations

• A review chapter without new empirical data; it surveys the current landscape and prospects rather than testing a specific system.

When this research may be relevant

This chapter may be relevant to literature on smart health and IoT-enabled healthcare, machine learning for healthcare analytics, ethical and security requirements of connected health devices, and reviews of digital health technology for patient-centred care.

Research topics addressed: smart health; healthcare analytics; machine learning in healthcare; Internet of Things; IoT in healthcare; early disease detection; personalised treatment; data privacy; model interpretability; bias mitigation; secure connectivity; global health disparities; healthcare informatics

How to cite

Vajjhala, N. R., & Eappen, P. (2024). Smart Health: Advancements in Machine Learning and the Internet of Things Solutions. In Pijush Samui, Sanjiban Sekhar Roy, Wengang Zhang, Y-h. Taguchi (Eds.), Machine Learning and IoT Applications for Health Informatics (pp. 31–51). CRC Press. https://doi.org/10.1201/9781003424987-3

@incollection{vajjhala2024smart,
  title = {Smart Health: Advancements in Machine Learning and the Internet of Things Solutions},
  author = {Vajjhala, Narasimha Rao and Eappen, Philip},
  booktitle = {Machine Learning and IoT Applications for Health Informatics},
  editor = {Pijush Samui and Sanjiban Sekhar Roy and Wengang Zhang and Y-h. Taguchi},
  pages = {31--51},
  year = {2024},
  publisher = {CRC Press},
  doi = {10.1201/9781003424987-3},
  url = {https://doi.org/10.1201/9781003424987-3}
}