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.
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.
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.
Early and accurate detection of mental health conditions is critical to enable timely therapeutic interventions among individuals diagnosed with AIDS. We introduce an innovative quantum‑augmented stacking ensemble that integrates a Quantum Support Vector Classifier with a Random Forest, leveraging quantum feature maps to accurately forecast depression risk in patients living with Acquired Immunodeficiency Syndrome (AIDS). Due to a lack of awareness and the persistence of AIDS-related stigma, patients may experience deteriorating mental health and adverse effects on their social lives. To assess mental health the parameters such as PHQ-9 Score, GAD-7 score, PTSD symptoms, Support System and Sought Therapy are being considered for the experimental result to detect early depression risk in patients. Further, to enhance transparency and diagnostic performance, we employ SHapley Additive exPlanations (SHAP) into our pipeline, yielding precise attributions of each feature’s influence on predictions and thereby strengthening model interpretability. This integration not only improves prediction reliability but also fosters clinical trust by applying SHapley Additive Explanations (SHAP) to the Quantum-Enhanced Stacking (QES) model, which achieved an ROC score of 0.91. SHAP assigns each feature a consistent, interpretable importance value by evaluating its marginal contribution to the model’s output. When applied to the QES framework, SHAP reveals how specific parameters such as GAD-7 scores and Support System drive significant individual predictions. This enhances transparency and aligns the model’s decision-making process with clinical reasoning.
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.
Nowadays, quantum machine learning is attracting great interest in a wide range of fields due to its potential superior performance and capabilities. The massive increase in computational capacity and speed of quantum computers can lead to a quantum leap in the healthcare field. Heart disease seriously threatens human health since it is the leading cause of death worldwide. Quantum machine learning methods can propose effective solutions to predict heart disease and aid in early diagnosis. In this study, an ensemble machine learning model based on quantum machine learning classifiers is proposed to predict the risk of heart disease. The proposed model is a bagging ensemble learning model where a quantum support vector classifier was used as a base classifier. Furthermore, in order to make the model’s outcomes more explainable, the importance of every single feature in the prediction is computed and visualized using SHapley Additive exPlanations (SHAP) framework. In the experimental study, other stand-alone quantum classifiers, namely, Quantum Support Vector Classifier (QSVC), Quantum Neural Network (QNN), and Variational Quantum Classifier (VQC) are applied and compared with classical machine learning classifiers such as Support Vector Machine (SVM), and Artificial Neural Network (ANN). The experimental results on the Cleveland dataset reveal the superiority of QSVC compared to the others, which explains its use in the proposed bagging model. The Bagging-QSVC model outperforms all aforementioned classifiers with an accuracy of 90.16% while showing great competitiveness compared to some state-of-the-art models using the same dataset. The results of the study indicate that quantum machine learning classifiers perform better than classical machine learning classifiers in predicting heart disease. In addition, the study reveals that the bagging ensemble learning technique is effective in improving the prediction accuracy of quantum classifiers.
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.
Timely clinical intervention at the early stage for detecting widespread psychological health issues such as depression and anxiety is essential. Conventional diagnostic approaches that are generally based on subjective questionnaires face limitations which can result in delays as well as imprecisions. Current data-driven methodologies employ features that are extracted from multiple sources (behavioural surveys, clinical attributes and physiological signals excluding EEG and ECG). Although known classical ML models such as RF and LR are effective, the limitations of these models on capturing extremely complex non-linear issues result in their performance being limited. This performance upper bound is validated by the combined classical benchmark accuracy of 86.59%. This paper introduces a new quantum hybrid support vector machine (QSVM) framework, and shows how it can dramatically improve prediction accuracy on such mental health conditions. The system uses a hybrid design, in which the classical processing part computes and sends out a processed feature map to be quantum mapped into a Q exponentially larger-dimensional Hilbert space through quantum kernel methods (Quantum Feature Maps). By taking advantage of the quantum effects, the QSVM implicitly computes kernels that are difficult for classical machines and allows achieving better classification boundaries. The accuracy of QSVM is critically evaluated against the RF and LR benchmarks. Thus The Hybrid QSVM shows preliminary signs of quantum advantage, particularly in precision and kernel separability, although accuracy remains comparable to classical baselines. Validation on real quantum hardware will be required to establish practical quantum advantage.