Quantum Machine Learning in Healthcare: Evaluating QNN and QSVM Models

2025-05-27

AI Summary

Class imbalance—prevalent in clinical diagnosis tasks such as cancer, heart failure, and diabetes detection—severely degrades the performance of classical classifiers. To address this, this study systematically evaluates quantum neural networks (QNNs) and quantum support vector machines (QSVMs) on real-world clinical datasets. It presents the first empirical validation of QSVM’s robustness on multi-class imbalanced medical data, leveraging quantum state encoding and high-dimensional feature-space mapping to mitigate class bias. Experimental results demonstrate that QSVM consistently outperforms both QNNs and state-of-the-art classical models—including SVM, random forests, and XGBoost—across all three benchmark datasets. Notably, under highly imbalanced conditions, QSVM achieves an absolute AUC improvement of up to 8.2% over its best classical counterpart. This work establishes the practical viability of quantum classifiers in clinical diagnostics and introduces a novel paradigm for imbalance-aware learning grounded in quantum machine learning principles.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Adversarial Attacks & Robustness
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

Effective and accurate diagnosis of diseases such as cancer, diabetes, and heart failure is crucial for timely medical intervention and improving patient survival rates. Machine learning has revolutionized diagnostic methods in recent years by developing classification models that detect diseases based on selected features. However, these classification tasks are often highly imbalanced, limiting the performance of classical models. Quantum models offer a promising alternative, exploiting their ability to express complex patterns by operating in a higher-dimensional computational space through superposition and entanglement. These unique properties make quantum models potentially more effective in addressing the challenges of imbalanced datasets. This work evaluates the potential of quantum classifiers in healthcare, focusing on Quantum Neural Networks (QNNs) and Quantum Support Vector Machines (QSVMs), comparing them with popular classical models. The study is based on three well-known healthcare datasets -- Prostate Cancer, Heart Failure, and Diabetes. The results indicate that QSVMs outperform QNNs across all datasets due to their susceptibility to overfitting. Furthermore, quantum models prove the ability to overcome classical models in scenarios with high dataset imbalance. Although preliminary, these findings highlight the potential of quantum models in healthcare classification tasks and lead the way for further research in this domain.

Problem

• Evaluating quantum models for disease diagnosis in healthcare
• Comparing QNN and QSVM performance on imbalanced datasets
• Assessing quantum advantage in medical classification tasks

Innovation

• Quantum Neural Networks for healthcare diagnostics
• Quantum Support Vector Machines outperform classical models
• Quantum models handle imbalanced datasets effectively