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Quantum-Enhanced Machine Learning for Early Disease Diagnosis: A Systematic Review and Public Health Innovation Perspective

Salman Mohammad Abdullah

M.S. in Information Technology (IT), Washington University of Science and Technology, Virginia, USA

Md Sajedul Karim Chy

M.S. in Project Management, Washington University of Science and Technology, Virginia, USA

Md. Salahuddin Gazi

B.S.S. (Honours) in Sociology, Jagannath University, Dhaka, Bangladesh

Abidul Alam

Bachelor of Business Administration (BBA), International University of Business, Agriculture, and Technology, Bangladesh

Md Himeluzzaman

M.Sc. in Information Technology Systems and Management, Washington University of Science and Technology, Virginia, USA

Tofayel Ahmed Onik

Doctorate in Computer Science, University of the Potomac, USA

Tanaya Jakir

Bachelor of Business Administration (Human Resources Management), State University of Bangladesh, Dhaka, Bangladesh

Abstract

Prompt diagnosis of disease is a strong driver for decreasing mortality, improving patient care and healthcare expenditures. Classical machine learning (ML), which has shown great potential in bolstering predictive modeling throughout oncology, cardiology, and neurology, runs the risk of excessive data dimensionality along with high computational complexity. Quantum computing is an exciting new paradigm for computation that takes advantage of superposition and entanglement which can be beneficial in learning patterns in high-dimensional spaces and for kernel-based learning. This review provides a systematic summary of the preliminary work on quantum machine learning (QML) with empirical applications to early disease diagnosis, while emphasizing its theoretical foundations, algorithmic developments and application in healthcare. We scope the conversation on quantum support vector machines, variational quantum classifiers, quantum kernels and hybrid architectures for a quantum classical. We also examine current hardware limitations in the NISQ era and assess how close these methods are to being ready for translation into public health applications. Although widespread clinical application is still far away, hybrid QML approaches are finding utility in low-data and high-complexity settings. We conclude with a discussion of research challenges, regulation and strategy towards realizing such quantum-enhanced precision diagnostics through U.S. public health innovation initiatives

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 6 No. 4 (2023): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2023.0604012

Pages

10384-10392

Published

July 20, 2023
Copyright

Open Access

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

How to Cite

Salman Mohammad Abdullah, Md Sajedul Karim Chy, Md. Salahuddin Gazi, Abidul Alam, Md Himeluzzaman, Tofayel Ahmed Onik, Tanaya Jakir (%2023). Quantum-Enhanced Machine Learning for Early Disease Diagnosis: A Systematic Review and Public Health Innovation Perspective. International Journal of Science, Research and Technology , Vol. 6 No. 4 (2023): International Journal of Science, Research and Technology (IJSRAT) , pp. 10384-10392. https://doi.org/10.15662/IJSRAT.2023.0604012

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