IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)
Quantum-Driven Predictive Analytics for Precision Medicine and Pharmacovigilance: A Multimodal Simulation Framework for Early Disease and Adverse Drug Event Detection
Amit Kumar Jaiswal, Bhawna Bisht, Harsh Kumar Yadav, Priyanshu Kumar
The computational intractability of the analysis of multimodal large-dimensional data, including genomics, electronic health records (EHR), proteomics, and real-world evidence, based on classical machine learning (ML) models, is the fundamental limitation of the precision medicine and proactive pharmacovigilance paradigm. Such models can also have problems modeling complex, non-linear feature interactions important to detect diseases at an early stage and to detect rare adverse drug events (ADEs). To address these shortcomings, this paper is suggesting a new hybrid quantumclassical framework of computation. We make use of the unique ability of the quantum computation, like superposition and entanglement to create more powerful predictive models. The encoding of the multimodal clinical and pharmacological data into quantum states (via specialized feature maps) followed by processing by Variational Quantum Classifiers (VQC) and Quantum Support Vector Machines (QSVM) on simulated quantum processors, is our framework. To benchmark, we made two major tasks: Type 2 Diabetes (T2D) early-stage prediction based on synthetic multimodal data and identification of rare ADE based on a curated selection of FDA Adverse Event Reporting System (FAERS). The outcome of the simulations indicates that our quantum-based models are always more effective than the classical models, such as the XGBoost and Deep Neural Networks, especially when it comes to sensitivity and F1 score in imbalanced class issues. QSVM model obtained an AUC ROC that is 7.3 percent greater than the optimal classical model, and a 15 percent greater recall than the optimal classical model in T2D prediction and rare ADE detection, respectively. This data supports the fact that quantum machine learning (QML) can be used to bring in a new dawn of predictive analytics in healthcare, and that it provides the way to more accurate, comprehensive, and proactive medical care.
The computational intractability of the analysis of multimodal large-dimensional data, including genomics, electronic health records (EHR), proteomics, and real-world evidence, based on classical machine learning (ML) models, is the fundamental limitation of the precision medicine and proactive pharmacovigilance paradigm. Such models can also have problems modeling complex, non-linear feature interactions important to detect diseases at an early stage and to detect rare adverse drug events (ADEs). To address these shortcomings, this paper is suggesting a new hybrid quantumclassical framework of computation. We make use of the unique ability of the quantum computation, like superposition and entanglement to create more powerful predictive models. The encoding of the multimodal clinical and pharmacological data into quantum states (via specialized feature maps) followed by processing by Variational Quantum Classifiers (VQC) and Quantum Support Vector Machines (QSVM) on simulated quantum processors, is our framework. To benchmark, we made two major tasks: Type 2 Diabetes (T2D) early-stage prediction based on synthetic multimodal data and identification of rare ADE based on a curated selection of FDA Adverse Event Reporting System (FAERS). The outcome of the simulations indicates that our quantum-based models are always more effective than the classical models, such as the XGBoost and Deep Neural Networks, especially when it comes to sensitivity and F1 score in imbalanced class issues. QSVM model obtained an AUC ROC that is 7.3 percent greater than the optimal classical model, and a 15 percent greater recall than the optimal classical model in T2D prediction and rare ADE detection, respectively. This data supports the fact that quantum machine learning (QML) can be used to bring in a new dawn of predictive analytics in healthcare, and that it provides the way to more accurate, comprehensive, and proactive medical care.
The healthcare systems produce a tremendous volume of medical data which may be in the form of patient records, laboratory test results and diagnostic reports. It is quite necessary to analyze such data accurately and efficiently in order to detect diseases early and plan appropriate treatment . Conventional machine learning methods have a tendency to struggling with large-dimensional and complicated medical data. To address these shortcomings, this project suggests a hybrid Quantum Machine Learning (QML) scheme of analyzing medical data and diagnosing a disease. The suggested system combines the classical data preprocessing with the quantum-enhanced data learning algorithms including Variational Quantum Circuits (VQC) and Quantum Support Vector Machines (QSVM). Medical data is first processed by cleaning, normalizing and encoding to quantum states. Complex patterns and correlations that traditional algorithms can hardly be able to learn are then learned with the quantum model. The measures used to judge the performance are accuracy, precision, recall and F1-score. The results of the experimental process show that the model based on QML yields better diagnostic accuracy than the conventional machine learning. This is where there is great potential in real time medical decision support systems, with the future prediction of disease and the personalized solution of care. The suggested framework indicates the potential and benefits of switching to quantum computing methods in healthcare analytics.
Quantum Machine Learning (QML) is fast becoming a powerful instrument in medical diagnostics because it can process high-dimensional data and small samples efficiently. This paper is a detailed analysis of QML models used for disease prediction, with a relative comparison of Variational Quantum Classifiers (VQC), Quantum Convolutional Neural Networks (QCNN), and Quantum Support Vector Machines (QSVM) in clinical environments. We suggest a new multimodal quantumclassical hybrid approach that analyzes electronic health records (EHR), medical imaging, and biosignal data by means of dedicated QML pipelines with late fusion for ultimate decisionmaking. Recent IEEE literature is integrated in experimental performance glimpses to demonstrate the merits, difficulties, and viability of QML in real-world healthcare applications. The envisioned architecture provides the foundation for future applications of quantum-enhanced diagnostics with existing quantum simulators as well as emerging hardware.
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 merger between Quantum Computing (QC) and Machine Learning (ML) will enable health analytics to be experienced in a way never seen before with unmatched efficiency and power. Conventional methods of healthcare data processing have not been able to work with very big and complex data sets that are related to medical imaging, genomics, and real-time tracking of the patients. Quantum Machine Learning (QML) has an advantage, as it uses new algorithms to work with such multidimensional data with much better accuracy, speed, and predictive power. This paper presents the essential contribution of QML to healthcare analytics, such as supporting diagnosis, to personalized medicine and predictive modelling. This quantum enhanced deep learning models will achieve what is currently being done at the larger scale in the field of medicine at a faster rate a thousand times, through earlier detection of diseases like cancer, Alzheimer disease and heart diseases. In drug discovery, quantum algorithms decrease the simulation of molecular interactions down to the atomic scale, which saves a substantial amount of time and cost of research. Security is, nevertheless, a massive issue since future developments in quantum theory will generate quantum-wise systems that will bring misfortunes to conventional encryption solutions adopted within healthcare systems. The implications of post-quantum cryptography that ensures the strong data privacy and safe patient records in quantum-powered ecosystem are discussed. Scalability obstacles, systems creation, and integration with traditional ML models, however, exist. Real-world applications and future perspectives on the quantum-enhanced healthcare analytics upon which plans have already been laid are presented. Looking ahead, QML will build in the transition of healthcare systems toward analytics frameworks faster, much more precise, and enhanced secure, heralding in the day of precision medicine early diagnostics and a new age for healthcare innovation.
Chronic diseases—such as diabetes, cardiovascular disorders, and chronic respiratory conditions—account for over 70% of global deaths annually, with late diagnosis being a primary contributor to poor outcomes. While machine learning (ML) models have shown promise in early detection, they often suffer from limited generalizability, data heterogeneity, and insufficient interpretability in clinical settings. This paper introduces a novel hybrid quantum-classical machine learning (HQML) framework that synergistically combines the pattern recognition power of classical deep neural networks with the high-dimensional optimization capabilities of quantum algorithms to enhance diagnostic accuracy, robustness, and early signal detection in chronic disease prediction. Using multimodal electronic health record (EHR) data—including clinical, genomic, and lifestyle variables—we train a quantum-enhanced feature selector followed by a classical interpretable classifier (e.g., SHAP-augmented XGBoost). The quantum component leverages a variational quantum circuit to identify non-linear, high-order feature interactions that classical methods often miss. Validated on datasets from the National Health and Nutrition Examination Survey (NHANES) and UK Biobank, our model achieves 94.7% AUC for type 2 diabetes prediction five years before clinical diagnosis—outperforming state-of-the-art baselines by 6.2%. Crucially, the framework maintains interpretability through post-hoc explainability and reduces data bias via fairness-aware quantum embedding. This research bridges quantum computing and public health, offering a scalable, ethically grounded diagnostic paradigm. By enabling earlier, more accurate, and equitable predictions, the HQML framework has significant potential to transform preventive care and reduce the global burden of chronic disease.
The cardiovascular and neurological conditions are one of the predominant causes of death and disability at a global scale, thus the need to develop smart healthcare systems to detect diseases at their initial stages or at all. The limitation of the conventional artificial intelligence-based and machine learning-based healthcare models are usually rife with high levels of computation complexity, low levels of prediction capabilities, poor scaling, and ineffective processing of complex healthcare data. The proposed solution to these problems is a Quantum Neural Network-Based Healthcare Analytics Framework that could be used to detect cardiovascular and neurological disorders at their initial stages. This framework is a combination of quantum computing and neural network models to improve diagnostic abilities in the field of healthcare analytics in disease classification, predictive accuracy, and computational efficiency. Experimental evaluation was done using healthcare datasets that comprised cardiovascular and neurological records of patients after being processed through preprocessing methods such as normalization, feature extraction and data balancing. Classification performance measures were used to assess the performance of the proposed model and these measures include Accuracy, Precision, Recall, Specificity, F1-Score, and AUC-ROC. The experimental findings showed that the suggested quantum neural network model worked much better than the traditional machine learning and deep learning models with regard to disease prediction accuracy, classification reliability, and the ability to detect disease early. Additionally, the framework had better sensitivity and lower false prediction, thus improving clinical decision-making support. The study helps expand the intelligent quantum healthcare analytics system to the next generation of medical diagnosis and predictive healthcare uses.