The growing occurrence of patients with multiple chronic diseases has increased the need for efficient multidisease risk prediction systems in healthcare. However, clinical data is often distributed in various cloud, but it lacks in efficient privacy preservation. To address these challenges, this project proposes a hybridized framework for privacy-aware multi-disease risk prediction in distributed healthcare environments. nA key challenge in multi-disease data collection from heterogeneous clinical data may lag in normalization. So, cleaning and preprocessing is done on the first phase. Second challenge lies in capturing complex and non-linear relationships among clinical features within reduced feature spaces. To handle this, quantum-inspired feature encoding and variational quantum circuits are employed to enhance feature representations. Since data is distributed across cloud data privacy and confidentiality issues exist. To overcome this issue Federated Learning for multi-disease prediction is applied in the third phase. To address these challenges, this paper proposes a novel framework titled Hybridized Quantum Computing and Federated Learning for Multi-Disease Prediction (HQCF-MDRP). The proposed system integrates structured data preprocessing, quantum-assisted feature representation, and federated collaborative learning into a unified architecture. Initially, healthcare datasets are cleaned, normalized, and subjected to feature selection to improve data quality and reduce dimensionality. Selected features are then transformed using quantum-inspired angle encoding and Variational Quantum Circuits (VQC), enabling enhanced representation of hidden correlations among clinical attributes. Subsequently, Federated Learning with FedProx optimization is employed to train predictive models collaboratively across distributed healthcare institutions without sharing raw patient data.
The growing occurrence of patients with multiple chronic diseases has increased the need for efficient multidisease risk prediction systems in healthcare. However, clinical data is often distributed in various cloud, but it lacks in efficient privacy preservation. To address these challenges, this project proposes a hybridized framework for privacy-aware multi-disease risk prediction in distributed healthcare environments. nA key challenge in multi-disease data collection from heterogeneous clinical data may lag in normalization. So, cleaning and preprocessing is done on the first phase. Second challenge lies in capturing complex and non-linear relationships among clinical features within reduced feature spaces. To handle this, quantum-inspired feature encoding and variational quantum circuits are employed to enhance feature representations. Since data is distributed across cloud data privacy and confidentiality issues exist. To overcome this issue Federated Learning for multi-disease prediction is applied in the third phase. To address these challenges, this paper proposes a novel framework titled Hybridized Quantum Computing and Federated Learning for Multi-Disease Prediction (HQCF-MDRP). The proposed system integrates structured data preprocessing, quantum-assisted feature representation, and federated collaborative learning into a unified architecture. Initially, healthcare datasets are cleaned, normalized, and subjected to feature selection to improve data quality and reduce dimensionality. Selected features are then transformed using quantum-inspired angle encoding and Variational Quantum Circuits (VQC), enabling enhanced representation of hidden correlations among clinical attributes. Subsequently, Federated Learning with FedProx optimization is employed to train predictive models collaboratively across distributed healthcare institutions without sharing raw patient data.
According to the World Health Organization, cardiovascular diseases are responsible for several million deaths worldwide each year, thus highlighting a prime necessity for developing methods of diagnosis that are accurate and rapid. Although recent advancements in quantum-enhanced machine learning have demonstrated enhanced prediction performance, current research is still limited by the reliance on static datasets, the lack of interpretability, and the lack of privacy-preserving mechanisms for data sharing across institutions. In this paper, we introduce an Explainable Quantum-Enhanced Federated Learning Framework for Real-Time Cardiovascular Risk Assessment that integrates the scalability of federated learning with the computational benefits of quantum models to allow multiple healthcare centers and remote devices to train models collaboratively without sharing raw patient data. Quantum-inspired classifiers such as variational circuits and Quantum SVM are integrated into the federated pipeline to accelerate convergence and improve robustness. An explanation layer was implemented to enhance clinical trust by applying changed feature attribution methods that help the physicians comprehend how much the important factors like cholesterol, blood pressure, and ECG patterns contribute to the risk predictions. In addition, the system is meant for constant watch by the mixing of electronic health records and wearable sensor streams, thus making it possible to switch from one-time prediction to dynamically and personally tailored risk assessment. The proposed technique has been validated through experiments on standard heart disease datasets where it is also said to have done the opposite: it has improved predictive accuracy, reduced communication overhead compared to traditional federated systems, and provided insights that are ready for supporting clinical decision-making in real practice. Consequently, this framework is a first step towards quantum-driven healthcare solutions that are safe, scalable, and explainable.
Abstract This study presents a Federated Quantum Variational Quantum Classifier (FedVQC) framework designed to address critical challenges in healthcare data privacy and computational efficiency, particularly in the domain of genomic analysis. Conventional machine learning approaches in healthcare often face obstacles stemming from privacy concerns, regulatory constraints, and the fragmented nature of medical data across different institutions. The proposed FedVQC framework leverages the principles of federated learning and quantum machine learning to enable secure, decentralized training of predictive models without requiring the sharing of sensitive patient data. Extensive experimentation on multiple genomic datasets reveals a strong correlation between the number of participating institutions and the model's accuracy, with performance consistently improving as more clients contribute to the training process. The framework demonstrates high accuracy levels, ranging from 70% to 96%, across diverse datasets, highlighting its robustness and applicability in healthcare analytics. By harnessing the computational power of quantum computing and the collaborative nature of federated learning, the FedVQC framework provides a scalable and secure solution for genomic research. This study underscores the potential of quantum federated learning in advancing privacy-preserving precision medicine, large-scale genomic collaboration, and broader applications in bioinformatics, drug discovery, and personalized healthcare analytics
In the healthcare sector, the scarcity of data and privacy concerns present formidable challenges to the widespread adoption of machine learning. In the present-day scenario, Federated Learning (FL) emerges as a pivotal solution, fostering the rapid evolution of distributed machine learning paradigms while adeptly addressing the problem of data governance and privacy. It allows distributed clients to collaboratively train a global model by synchronizing their local updates without sharing private data. In recent years, federated learning and quantum computing have individually shown great promise to revolutionize various sectors, including healthcare, finance, and manufacturing, where privacy protection is paramount. In this article, we propose a communication-efficient Quantum Federated Learning (QFL) framework based on a variational circuit that enables clients to efficiently train and transmit quantum model parameters, thereby reducing communication rounds significantly and enhancing QFL performance using quantum natural gradient descent (QNGD) optimization. This paper demonstrates the feasibility of a QFL framework for predicting the presence of coronary heart disease, diagnosing whether a patient is suffering from diabetes or not, and differentiating malignant and benign cancer by distributing the UCI datasets unbalanced among healthcare institutions. The proposed framework has the potential to incorporate privacy, security, and the expedited processing of distributed data. QNGD outperformed classical GD by reducing communication rounds by a range of 5% to 60%. In addition to reducing the communication rounds by optimizing the QFL training algorithm and achieving quicker convergence, it also determines the important features regardless of the data imbalance among the clients.
Abstract Healthcare organizations have a high volume of sensitive data and traditional technologies have limited storage capacity and computational resources. The prospect of sharing healthcare data for machine learning is more arduous due to firm regulations related to patient privacy. The balanced protection of confidentiality, integrity, and availability of healthcare data, has become a major concern beyond classical data security considerations. In recent years, federated learning offers a solution to accelerate distributed machine learning addressing concerns related to data privacy and governance. Currently, the blend of quantum computing and machine learning has experienced significant attention from academic institutions and research communities. Quantum computers have shown the potential to bring huge benefits to the healthcare sector through efficient distributed training across several quantum nodes. The ultimate objective of this work is to develop a quantum federated learning framework (QFL) to tackle the optimization, security, and privacy challenges in the healthcare and clinical industries for medical imaging tasks. In this work, we proposed federated quantum convolutional neural networks (QCNNs) with distributed training across edge devices. To demonstrate the feasibility of the proposed QFL framework, we performed extensive experiments on medical datasets (Pneumonia MNIST, and CT-kidney disease analysis), which are non-independently and non-identically partitioned among the healthcare institutions/clients. The quantum federated global model maintained a high classification testing accuracy and generalizability and outperformed the locally train clients regardless of how unbalanced the medical data is distributed among the clients. The global model achieved the best performance as compared to local clients in terms of the area under curve of the receiver operating characteristic curve (AUCROC) (0.953) and an average of (0.98) on all classes for predicting outcomes on pneumonia and CT-kidney datasets, respectively. Moreover, the client selection mechanism is proposed to reduce the computation overhead at each communication round, which effectively improves the convergence rate. The proposed quantum federated learning framework is validated and assessed via large-scale simulations. Based on our results from numerical simulations, the deployment of distributed and secure quantum machine learning algorithms for enabling scalable and privacy-preserving intelligent healthcare applications would be extremely valuable.
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.
The growing prevalence of mental stress has created a strong need for intelligent systems capable of early and accurate detection. This work proposes a hybrid learning framework that combines classical machine learning, quantum computing techniques, and federated learning to classify stress levels in in-dividuals. Initially, physiological signalsfrom the WESAD dataset are processed to remove noise and normalize feature values. A Random Forest model is then applied to identify the most influential features, which helps reduce data dimensionality and makes the model suitable for quantum processing. The selected features are transformed into quantum states using rotation-based angle encoding with RY gates. A variational quantum neural network is designed to learn patterns within the encoded data through parameterized quantum circuits. To ensure data privacy and enable decentralized training, a federated learning strategy is incorporated, where multiple clients train models locally and share only model parameters. These parameters are combined using a federated averaging method to form a global model without exposing sensitive data. The developed system categorizes mental stress into three levels: low, medium, and high. The results indicate that integrating quantum learning with federated approaches can effectively handle sensitive health data while maintaining reliable classification performance. This study demonstrates the potential of combining emerging computational paradigms to build secure and efficient healthcare prediction systems.