Multi-VQC: A Novel QML Approach for Enhancing Healthcare Classification
This addresses the problem of class imbalance in healthcare classification for medical practitioners and patients, though it appears incremental as it builds on existing quantum machine learning concepts.
The paper tackles disease diagnosis classification problems that suffer from significant class imbalances by proposing a novel quantum machine learning approach called Multi-VQC, which aims to overcome limitations of traditional models by leveraging quantum computing's ability to express complex patterns in higher-dimensional spaces.
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.