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arXiv:2410.21339

Machine Learning and Quantum Intelligence for Health Data Scenarios

Sanjeev Naguleswaran

Published Oct 28, 2024• 1 citation
arXivHTMLPDF

Abstract

The advent of quantum computing has opened new possibilities in data science, offering unique capabilities for addressing complex, data-intensive problems. Traditional machine learning algorithms often face challenges in high-dimensional or limited-quality datasets, which are common in healthcare. Quantum Machine Learning leverages quantum properties, such as superposition and entanglement, to enhance pattern recognition and classification, potentially surpassing classical approaches. This paper explores QML's application in healthcare, focusing on quantum kernel methods and hybrid quantum-classical networks for heart disease prediction and COVID-19 detection, assessing their feasibility and performance.

DOI
10.48550/arxiv.2410.21339

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The exploration of quantum neural networks for healthcare applications also necessitates rigorous ethical considerations, particularly in ensuring data privacy and security. The incorporation of quantum computing must comply with healthcare data protection regulations, and quantum algorithms must be designed to maintain patient confidentiality while processing sensitive health data. Moreover, the interpretability of quantum models poses challenges that could hinder their acceptance in clinical practice. Advances in explainable AI and quantum algorithm transparency are crucial to foster trust among healthcare professionals and patients alike. Integration of quantum neural networks with classical machine learning models represents a transformative approach that could significantly advance healthcare diagnostics, personalized treatment strategies, and patient outcome prediction. 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This paper explores the use of quantum-based machine learning algorithms for predicting heart disease. Traditional methods face challenges in processing complex healthcare data efficiently. To address this, quantum computing techniques, including dimensionality reduction and feature optimization, are applied. Quantum machine learning models, such as Variational Quantum Classifier (VQC), Quantum Support Vector Classification (QSVC) and Quantum Neural Networks (QNN), leverage these enriched datasets. This convergence of quantum computing and healthcare has the potential to transform medical care, cut costs, and save lives, presenting a significant advancement in predictive medicine.