Introduction Nearly 15% of couples worldwide experience infertility, with Male Infertility (MI) accounting for 30–50% of these cases. Despite this burden, research on MI lags behind female infertility, particularly in understanding how demographic, clinical, lifestyle, and environmental factors interact to shape reproductive outcomes. Traditional approaches typically examine isolated indicators and struggle to capture the high-dimensional, nonlinear patterns present in Electronic Health Record (EHR) data. This study proposes a data-driven, interpretable framework integrating global fertility rate trends with EHR-derived male fertility data to (i) identify key determinants of MI, (ii) compare multiple classical Long Short-Term Memory (LSTM) architectures against a novel Quantum LSTM model embedding a Variational Quantum Circuit (VQC) directly within the LSTM gating mechanism, and (iii) evaluate the feasibility of quantum-enhanced sequential learning on small, heterogeneous healthcare datasets. Methods Two public datasets were used: a global fertility rate time series (1960–2020) and an EHR–derived male fertility dataset ( n = 100, 10 features). After preprocessing (scaling, encoding, multicollinearity checks, and class-imbalance correction), several classical LSTM variants and the proposed Quantum LSTM were trained and validated using 5-fold cross-validation, repeated random train–test splits, and bootstrap resampling. Performance was assessed via accuracy, precision, recall, F1-score, and computational time, with statistical significance evaluated using Friedman and Wilcoxon signed-rank tests. SHAP (SHapley Additive exPlanations) was applied for feature-level and model-level interpretability. Results Among classical models, unilateral LSTM performed most consistently (91.2% accuracy on fertility-rate data; 92.1% on male-fertility data). The Quantum LSTM achieved the highest accuracy on both datasets (92.6% and 93.8% respectively), a modest but statistically significant improvement (Wilcoxon p = 0.031 and p = 0.024; Cohen's d = 0.42–0.48) with narrower bootstrap confidence intervals, indicating greater stability. SHAP analysis identified daily sitting duration and age as the most influential predictors of male fertility outcomes, followed by season and alcohol consumption frequency, with clinical history variables contributing comparatively less. Discussion These findings demonstrate the feasibility of embedding quantum circuits within LSTM gating for small-scale healthcare prediction tasks, yielding incremental rather than transformative gains over classical architectures. Observed associations are exploratory and non-causal, and given the limited sample size ( n = 100), single-center source, and simulator-based quantum execution, results should be interpreted as hypothesis-generating rather than clinically conclusive. This work establishes a reproducible benchmarking framework combining classical and quantum-enhanced LSTM models with SHAP-based explainability for early MI risk stratification; future work should validate findings on larger, multi-center cohorts and real quantum hardware.
Introduction Nearly 15% of couples worldwide experience infertility, with Male Infertility (MI) accounting for 30–50% of these cases. Despite this burden, research on MI lags behind female infertility, particularly in understanding how demographic, clinical, lifestyle, and environmental factors interact to shape reproductive outcomes. Traditional approaches typically examine isolated indicators and struggle to capture the high-dimensional, nonlinear patterns present in Electronic Health Record (EHR) data. This study proposes a data-driven, interpretable framework integrating global fertility rate trends with EHR-derived male fertility data to (i) identify key determinants of MI, (ii) compare multiple classical Long Short-Term Memory (LSTM) architectures against a novel Quantum LSTM model embedding a Variational Quantum Circuit (VQC) directly within the LSTM gating mechanism, and (iii) evaluate the feasibility of quantum-enhanced sequential learning on small, heterogeneous healthcare datasets. Methods Two public datasets were used: a global fertility rate time series (1960–2020) and an EHR–derived male fertility dataset ( n = 100, 10 features). After preprocessing (scaling, encoding, multicollinearity checks, and class-imbalance correction), several classical LSTM variants and the proposed Quantum LSTM were trained and validated using 5-fold cross-validation, repeated random train–test splits, and bootstrap resampling. Performance was assessed via accuracy, precision, recall, F1-score, and computational time, with statistical significance evaluated using Friedman and Wilcoxon signed-rank tests. SHAP (SHapley Additive exPlanations) was applied for feature-level and model-level interpretability. Results Among classical models, unilateral LSTM performed most consistently (91.2% accuracy on fertility-rate data; 92.1% on male-fertility data). The Quantum LSTM achieved the highest accuracy on both datasets (92.6% and 93.8% respectively), a modest but statistically significant improvement (Wilcoxon p = 0.031 and p = 0.024; Cohen's d = 0.42–0.48) with narrower bootstrap confidence intervals, indicating greater stability. SHAP analysis identified daily sitting duration and age as the most influential predictors of male fertility outcomes, followed by season and alcohol consumption frequency, with clinical history variables contributing comparatively less. Discussion These findings demonstrate the feasibility of embedding quantum circuits within LSTM gating for small-scale healthcare prediction tasks, yielding incremental rather than transformative gains over classical architectures. Observed associations are exploratory and non-causal, and given the limited sample size ( n = 100), single-center source, and simulator-based quantum execution, results should be interpreted as hypothesis-generating rather than clinically conclusive. This work establishes a reproducible benchmarking framework combining classical and quantum-enhanced LSTM models with SHAP-based explainability for early MI risk stratification; future work should validate findings on larger, multi-center cohorts and real quantum hardware.
Abstract Maternal mental health disorders remain a major public health problem, particularly in low- and middle-income countries, where limited mental health resources and inadequate screening systems lead to late diagnosis and intervention. Recent advances in artificial intelligence have demonstrated great potential in improving the prediction of early maternal mental health risks. However, most current methods are limited to traditional machine learning models and lack interpretability. This study introduces the explainable Hybrid Classical-Quantum Support Vector Machine (SVM) framework for the prediction of maternal mental health risk using multicountry psychosocial and clinical data from Uganda and Pakistan. The proposed framework integrates classical SVM learning with quantum-enhanced feature representations, while SHapley Additive exPlanations (SHAP) are leveraged to increase the model transparency and clinical interpretability. A comparative evaluation was performed against Random Forest, XGBoost, LightGBM, CatBoost, Stacking Ensemble, Classical SVM, and standalone Quantum SVM using Accuracy, Precision, Recall, F1-score, Receiver Operating Characteristic Area Under the Curve (ROC-AUC) and Precision-Recall Area Under the Curve (PR-AUC). We performed two experimental settings: one using the full set of features and another using a set of features without leakage, i.e. excluding variables that directly contribute to the EPDS-derived outcome. Under the complete feature set, the proposed Hybrid Classical-Quantum SVM achieved 99.86% accuracy, 99.80% F1-score, 99.92% ROC-AUC and 99.91% PR-AUC, while maintaining competitive performance after the leakage-free evaluation. The SHAP analysis provided enhanced model interpretability, identifying clinically meaningful demographic, obstetric, socioeconomic and psychosocial factors associated with maternal mental health risk. Our results demonstrate that hybrid classical-quantum learning offers a competitive and explainable paradigm for predicting maternal mental health risk and highlights the complementary nature of quantum feature representations in healthcare analytics.
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.
Abstract Heart disease remains to be a primary cause of death globally, requiring precise and timely predictive methodologies. Traditional machine learning methods that depend exclusively on classical clinical variables frequently have restricted predictive accuracy and do not adequately use the intricate nonlinear interactions present in patient data. This work examines the effectiveness of hybrid quantum-classical models for predicting heart disease by combining classical clinical data with quantum-derived representations via Variational Quantum Classifier (VQC), Estimator, and Sampler models. Twelve hybrid configurations, integrating three quantum feature maps (Z, ZZ, Pauli) with four ansatz architectures (RealAmplitudes, TwoLocal, EfficientSU2, PauliTwoDesign), were assessed inside the PennyLane and Qiskit frameworks. The results indicate that the VQC model in PennyLane reached optimal performance with Z–TwoLocal and ZZ–PauliTwoDesign configurations, achieving accuracy and F1-scores of up to 0.95. The Estimator model consistently produced reliable predictions, with Pauli–EfficientSU2 and Z–TwoLocal attaining accuracies of up to 0.91 and F1-scores of 0.92 across several frameworks. The Sampler model demonstrated 1reasonable performance, with the Pauli–EfficientSU2 configuration achieving an accuracy of 0.65 and an F1-score of 0.73, underscoring the supplementary role of stochastic quantum sampling. The analysis of feature significance indicated that the quantum-derived feature provided the most robust predictive signal, exceeding classical variables like Age, Blood Pressure, and Cholesterol. These findings demonstrate that meticulous alignment of feature maps and ansatz circuits is essential for optimizing model expressivity, convergence, and predictive effectiveness. In summary, hybrid quantum-classical models, especially the Estimator and VQC architectures, significantly improve heart disease prediction, providing a feasible method for precision clinical decision support and potential incorporation into privacy-preserving, multi-institutional healthcare systems.
Purpose This study investigates the integration of quantum-enhanced machine learning (QML) into medical image analysis, focusing on classification and feature extraction tasks. It aims to evaluate whether hybrid quantum-classical models can overcome the limitations of traditional machine learning in handling high-dimensional, clinically relevant imaging data. Design/methodology/approach Hybrid architectures combining classical preprocessing with quantum components, specifically variational quantum circuits (VQCs) and quantum kernel methods, were developed and implemented in simulated quantum environments using Qiskit and PennyLane on Google Colab. Publicly available datasets (ChestX-ray14, Brain Tumor MRI and CT Hemorrhage) were preprocessed and encoded into quantum states. Model performance was evaluated against classical baselines (MLP and CNN) using accuracy, precision, recall, F1-score, convergence speed and trainable parameters. Findings Quantum-enhanced models achieved competitive or superior performance compared to classical models with significantly fewer trainable parameters and faster convergence. The Quantum Kernel SVM demonstrated the highest precision (89.1%) and F1-score (88.5%) using only 8 parameters, while VQC models converged up to 2× faster than classical baselines. These results highlight the potential of QML to improve efficiency, robustness and scalability in diagnostic imaging workflows, particularly in resource-constrained or data-limited settings. Research limitations/implications The study relies on simulated quantum environments, which do not capture hardware noise or qubit limitations. Future work will extend to real quantum devices, multimodal data integration and interpretability strategies. Practical implications Quantum kernel methods and lightweight VQC architectures enable faster, resource-efficient diagnostic modeling, making them suitable for clinical environments with limited computational capacity or time-sensitive decision-making needs. Social implications Improving the efficiency and accessibility of medical image analysis has the potential to support faster diagnosis and enhance healthcare delivery, particularly in underserved or resource-limited regions. Originality/value This study provides a practical evaluation of hybrid quantum-classical models applied to multiple real medical imaging datasets in a reproducible simulated environment. It offers an early-stage framework demonstrating how QML techniques can be integrated into existing machine learning pipelines for medical image analysis.
Early and accurate prediction of cardiovascular disease is essential to improve patient outcomes and reduce healthcare costs. This research presents a hybrid classical–quantum machine learning framework for heart disease prediction using the Cleveland dataset. The proposed pipeline integrates advanced feature engineering, bio-inspired optimization, and quantum-inspired learning to improve classification performance and interpretability. The system applies multiple feature selection techniques followed by a hybrid feature fusion strategy. Orthogonal Component Analysis is then used for dimensionality transformation, while quantum-inspired feature mapping simulates quantum state coding. A feature selection mechanism based on a Genetic Algorithm optimizes the subset of features. Classical and quantum machine learning models are evaluated, including Random Forest, Gradient Boosting, K-Nearest Neighbors, Logistic Regression, Quantum Support Vector Classifier, Variational Quantum Classifier, Quantum KNN, and Quantum Neural Networks. Model performance is evaluated using accuracy metrics. To ensure transparency and trustworthiness, explainable AI techniques such as SHAP, LIME and DiCE are integrated to provide local and global interpretability of predictions. Experimental results demonstrate that the proposed hybrid framework improves predictive performance by achieving 90% accuracy compared to traditional machine learning approaches, while maintaining model explainability. The model achieved an overall accuracy of 90%, indicating strong predictive capability in cardiovascular disease risk classification. A detailed analysis of class-wise performance shows that for Class 0, the model obtained a precision of 0.85, a recall of 0.97, and an F1-score of 0.90, demonstrating excellent ability to correctly identify negative cases with minimal false negatives. For Class 1, the model achieved a precision of 0.96, a recall of 0.84, and an F1-score of 0.90, indicating high confidence in positive predictions, though with slightly lower recall compared to Class 0. This study highlights the potential of combining classical feature engineering, evolutionary optimization and quantum-inspired learning for next-generation medical decision support systems. The integration of quantum-inspired techniques also provides a promising direction for improving computational efficiency and model robustness in healthcare analytics. The findings suggest that hybrid classical–quantum learning approaches can support clinicians in making faster and more reliable diagnostic decisions.
The rapid growth of healthcare data demands predictive models capable of handling complexity, noise, and imbalance inherent in clinical datasets. This study explores the integration of quantum computing with artificial intelligence to develop Hybrid Quantum–AI models for healthcare prediction. The proposed framework combines classical preprocessing and feature extraction with quantum neural networks (QNNs) and quantum kernel methods to enhance predictive performance on diverse healthcare tasks, including disease diagnosis and postoperative complication prediction. Empirical evaluation across benchmark and clinical datasets demonstrates that hybrid quantum models achieve superior sensitivity, precision, and calibration compared to traditional machine learning approaches, particularly under conditions of data imperfection and small sample size. Beyond performance, this work investigates practical barriers to clinical adoption, including hardware limitations, scalability, interpretability, and ethical compliance. The results highlight that while current quantum hardware remains a constraint, hybrid approaches already offer tangible benefits in predictive accuracy and robustness. The study concludes by outlining a roadmap for real-world implementation, emphasizing the need for interpretable hybrid architectures, federated data strategies, and regulatory alignment to enable the transition of quantum–AI healthcare solutions from research to clinical practice.