Multi-VQC: A Novel QML Approach for Enhancing Healthcare Classification

TL;DR

This paper introduces Multi-VQC, a quantum machine learning approach designed to improve disease classification accuracy by addressing class imbalance issues in healthcare diagnostics.

Contribution

The paper presents a novel quantum machine learning model that enhances disease classification performance, leveraging quantum computing to better handle complex data patterns and class imbalances.

Findings

Improved classification accuracy over classical models

Effective handling of class imbalance in healthcare data

Demonstrated potential of quantum models in medical diagnostics

Abstract

Accurate and reliable diagnosis of diseases is crucial in enabling timely medical treatment and enhancing patient survival rates. In recent years, Machine Learning has revolutionized diagnostic practices by creating classification models capable of identifying diseases. However, these classification problems often suffer from significant class imbalances, which can inhibit the effectiveness of traditional models. Therefore, the interest in Quantum models has arisen, driven by the captivating promise of overcoming the limitations of the classical counterpart thanks to their ability to express complex patterns by mapping data in a higher-dimensional computational space.