Quantum Machine Learning in Healthcare: Evaluating QNN and QSVM Models

TL;DR

This paper evaluates quantum classifiers, specifically QNNs and QSVMs, in healthcare diagnosis tasks, demonstrating that QSVMs outperform QNNs and can better handle imbalanced datasets, indicating promising future applications.

Contribution

It provides a comparative analysis of quantum and classical models in healthcare, highlighting the advantages of QSVMs in imbalanced data scenarios.

Findings

QSVMs outperform QNNs across datasets

Quantum models better handle dataset imbalance

Quantum models show potential for healthcare classification

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.