| Title |
A hybrid quantum-classical approach for liver disease detection using quantum machine learning |
| Authors |
Donaire, Laura María ; Ortega, Gloria ; Orts, Francisco ; Garzón, Ester Martín ; Filatovas, Ernestas |
| DOI |
10.1016/j.engappai.2025.113240 |
| Full Text |
|
| Is Part of |
Engineering applications of artificial intelligence.. Oxford : Elsevier Ltd. 2026, vol. 164, pt. A, art. no. 113240, p. [1-14].. ISSN 0952-1976 |
| Keywords [eng] |
liver diseases ; machine learning ; quantum computing ; quantum layers ; quantum machine learning |
| Abstract [eng] |
Quantum Machine Learning (QML) combines principles of quantum computing with traditional Machine Learning (ML) to explore computational advantages in data processing and model efficiency. With the rise of Noisy Intermediate-Scale Quantum (NISQ) devices, hybrid quantum–classical approaches are gaining momentum, especially in domains requiring high precision such as healthcare. In this work, we investigate whether hybrid quantum computing can enhance certain aspects of classical ML, specifically in dataset balancing and the complexity of the neural network involved in training. To this end, we use the Indian Liver Patient Dataset as a case study to determine the presence of liver disease. We present the methodology for developing ‘QML-Liver’, a hybrid approach that seamlessly integrates classical and QML techniques. This includes data preprocessing, model design, and optimal configuration. Our results demonstrate that ‘QML-Liver’ improves key performance metrics, such as accuracy and F1-Score. Additionally, we successfully reduce the number of required qubits to just two, making practical deployment more feasible. These findings underscore the potential of QML for medical diagnostics, particularly in the NISQ era. |
| Published |
Oxford : Elsevier Ltd |
| Type |
Journal article |
| Language |
English |
| Publication date |
2026 |
| CC license |
|
