Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Towards Trustworthy Quantum Machine Learning in Clinical Diagnostics: A Multi-Method Explainability Study of a Hybrid Quantum-Classical Neural Network for Liver Disease Detection

Fatima Masood

Published Jul 12, 2026• 0 citations
Publisher
DOI
10.13140/rg.2.2.11569.34403

Related papers

A Focus on Hybrid Ensemble Quantum Machine Learning: Classifying Chronic Liver Disease

Mayank Raj, Ramasamy Sasikala, Thanisha Ganesh +3 more

Lecture notes in networks and systems•Jan 1, 2026•0 citations

Liver Disease Diagnosis Using Quantum-based Binary Neural Network Learning Algorithm

Om Prakash Patel, Aruna Tiwari

Advances in intelligent systems and computing•Dec 23, 2014•5 citations

Quantum-Enhanced Neuromorphic Framework for Early Detection of Wilson's Disease Using SNN-QSVM Hybrid Modeling

S. Priyanga, G. Nithila, S. Baghavathi Priya +1 more

Neurological disorders often cause progressive cognitive and motor impairments, with autosomal recessive diseases presenting diagnostic challenges due to overlapping symptoms and gradual onset. Wilson's Disease (WD), caused by ATP7B gene mutations, leads to toxic copper accumulation in the liver and brain, resulting in hepatic, neurological, and psychiatric dysfunction. Conventional diagnostic methods, such as serum ceruloplasmin tests, copper assays, and liver biopsies, are invasive and inconsistent in early detection. To address these limitations, this work proposes a hybrid Spiking Neural Network–Quantum Support Vector Machine (SNN-QSVM) framework for WD classification. The model uses neuromorphic learning and quantum-enhanced classification on demographic, clinical, symptomatic, genetic, and lifestyle features from a 60,000-sample dataset. Evaluated using accuracy, precision, recall, F1-score, and AUC-ROC, the framework achieved 94% accuracy, demonstrating strong diagnostic capability. Future work includes multimodal clinical integration and scalable quantum deployment.

Advances in computational intelligence and robotics book series•Jun 24, 2026•0 citations

A hybrid quantum-classical approach for liver disease detection using quantum machine learning

Laura María Donaire, Gloria Ortega, Francisco José Orts +2 more

Quantum Machine Learning (QML) combines principles of quantum computing with tradicional 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 machine learning, 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 quantum machine learning techniques. This includes data preprocessing, model design, and optimal configuration. Our results demonstrate that ‘QML-Liver’ improves key performance metrics, such as accuracy and precision. Additionally, we successfully reduce the number of required qubits to just one, making practical deployment more feasible. These findings underscore the potential of Quantum Machine Learning for medical diagnostics, particularly in the NISQ era.

Engineering Applications of Artificial Intelligence•Nov 22, 2025•6 citations

Quantum Machine Learning Algorithms for Diagnostic Applications: A Review

Shruti S. Pophale, Amit Gadekar

Lecture notes in electrical engineering•Jan 1, 2023•3 citations

Early Detection of Chronic Diseases Using Quantum Machine Learning Algorithms

N. Deepika, P. RaniAnnapurna, T. Satya Kavyanjali +2 more

Early diagnosis of chronic diseases is paramount to improving patient survival rates, optimizing treatment trajectories, and mitigating escalating healthcare expenditures. While the recent explosion of heterogeneous healthcare data presents robust opportunities for predictive modeling, it simultaneously introduces severe computational bottlenecks regarding data dimensionality and non-linear complexity. This paper proposes a novel, high-performance hybrid prediction framework that seamlessly fuses classical machine learning topologies with Variational Quantum Circuits (VQCs) within a Quantum Machine Learning (QML) paradigm. The proposed architecture targets the multi-classification and risk profiling of five high-prevalence chronic conditions: diabetes, cardiovascular disease, breast cancer, chronic liver disease, and chronic kidney disease This project introduces “Quantum Health,” a Quantum Machine Learning (QML)-based multi disease prediction system that integrates classical and quantum computing to enhance diagnostic performance. It employs five quantum-enhanced models: Quantum Random Forest for breast cancer, Quantum Neural Network for diabetes, Quantum k-Nearest Neighbors (QkNN) for heart disease, Quantum AdaBoost for liver disease, and Quantum Naive Bayes for kidney disease. The models are evaluated using both classical methods and PennyLane simulations, enabling direct comparison of performance. The system is deployed as a Flask-based web application featuring a user-friendly dashboard, disease-specific prediction modules, prediction history with CSV export, and an analytics dashboard comparing classical and quantum accuracies. Each module provides predictions, confidence scores, and feature importance insights. Unlike monolithic classical models, our approach incorporates a parameterized quantum layer that maps low-dimensional classical medical vectors into high-dimensional Hilbert spaces, capturing intricate, non-linear correlations that evade classical kernels. Experimental evaluations executed on simulated quantum backends demonstrate that the proposed Quantum Hybrid Model drastically outperforms conventional baselines, achieving an overall classification accuracy of 96.8% and an Area Under the Curve (AUC) of 98.4%. The findings establish that integrating quantum circuit state vectors with ensemble classical architectures provides a scalable, computationally efficient, and highly generalizable paradigm for next-generation clinical decision support systems.

SPU Journal of Science Technology and Management Research•Jul 1, 2026•0 citations
Neurological disorders often cause progressive cognitive and motor impairments, with autosomal recessive diseases presenting diagnostic challenges due to overlapping symptoms and gradual onset. Wilson's Disease (WD), caused by ATP7B gene mutations, leads to toxic copper accumulation in the liver and brain, resulting in hepatic, neurological, and psychiatric dysfunction. Conventional diagnostic methods, such as serum ceruloplasmin tests, copper assays, and liver biopsies, are invasive and inconsistent in early detection. To address these limitations, this work proposes a hybrid Spiking Neural Network–Quantum Support Vector Machine (SNN-QSVM) framework for WD classification. The model uses neuromorphic learning and quantum-enhanced classification on demographic, clinical, symptomatic, genetic, and lifestyle features from a 60,000-sample dataset. Evaluated using accuracy, precision, recall, F1-score, and AUC-ROC, the framework achieved 94% accuracy, demonstrating strong diagnostic capability. Future work includes multimodal clinical integration and scalable quantum deployment.
Quantum Machine Learning (QML) combines principles of quantum computing with tradicional 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 machine learning, 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 quantum machine learning techniques. This includes data preprocessing, model design, and optimal configuration. Our results demonstrate that ‘QML-Liver’ improves key performance metrics, such as accuracy and precision. Additionally, we successfully reduce the number of required qubits to just one, making practical deployment more feasible. These findings underscore the potential of Quantum Machine Learning for medical diagnostics, particularly in the NISQ era.
Early diagnosis of chronic diseases is paramount to improving patient survival rates, optimizing treatment trajectories, and mitigating escalating healthcare expenditures. While the recent explosion of heterogeneous healthcare data presents robust opportunities for predictive modeling, it simultaneously introduces severe computational bottlenecks regarding data dimensionality and non-linear complexity. This paper proposes a novel, high-performance hybrid prediction framework that seamlessly fuses classical machine learning topologies with Variational Quantum Circuits (VQCs) within a Quantum Machine Learning (QML) paradigm. The proposed architecture targets the multi-classification and risk profiling of five high-prevalence chronic conditions: diabetes, cardiovascular disease, breast cancer, chronic liver disease, and chronic kidney disease This project introduces “Quantum Health,” a Quantum Machine Learning (QML)-based multi disease prediction system that integrates classical and quantum computing to enhance diagnostic performance. It employs five quantum-enhanced models: Quantum Random Forest for breast cancer, Quantum Neural Network for diabetes, Quantum k-Nearest Neighbors (QkNN) for heart disease, Quantum AdaBoost for liver disease, and Quantum Naive Bayes for kidney disease. The models are evaluated using both classical methods and PennyLane simulations, enabling direct comparison of performance. The system is deployed as a Flask-based web application featuring a user-friendly dashboard, disease-specific prediction modules, prediction history with CSV export, and an analytics dashboard comparing classical and quantum accuracies. Each module provides predictions, confidence scores, and feature importance insights. Unlike monolithic classical models, our approach incorporates a parameterized quantum layer that maps low-dimensional classical medical vectors into high-dimensional Hilbert spaces, capturing intricate, non-linear correlations that evade classical kernels. Experimental evaluations executed on simulated quantum backends demonstrate that the proposed Quantum Hybrid Model drastically outperforms conventional baselines, achieving an overall classification accuracy of 96.8% and an Area Under the Curve (AUC) of 98.4%. The findings establish that integrating quantum circuit state vectors with ensemble classical architectures provides a scalable, computationally efficient, and highly generalizable paradigm for next-generation clinical decision support systems.