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.
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.
Our research introduces a state-of-the-art bagging ensemble model that leverages Quantum Support Vector Classifiers (QSVC) to assess the risk of heart disease. Testing involved rigorous experimentation on the Cleveland data set for which QSVC has demonstrated better performance compared with benchmark classifiers and other quantum-inspired models as well, achieving an excellent accuracy of 90.16%. The boosted accuracy shows the competence of quantum machine learning in serving as a potential upgrade of predictive analytics within the healthcare industry. In addition to its impressive performance metrics, the model incorporates SHAP (SHapley Additive exPlanations) analysis, which significantly enhances its interpretability. By identifying and elucidating the utmost need of various features in this entire route, SHAP analysis makes the persons who are into medical practicing get to know more about the core of this model. This transparency is crucial in medical applications, where understanding the factors influencing a diagnosis can lead to better key decisions and sentiments of patients. Thus, Implications of this research extend beyond mere performance statistics. The research shows that quantum machine learning, and specifically QSVC, can revolutionize healthcare by providing interpretable and accurate predictive models. Over time, the technology will keep improving, and ultimately it will improve patient care and the quality of healthcare.
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.
The increasing cases of mental illnesses such as stress, anxiety, and depression, driven by excessive workload, social pressure, and emotional instability, highlight the need for early and accurate detection. Traditional Machine Learning (ML) and Deep Learning (DL) models were initially used for this purpose, as they can analyse psychological datasets like DASS-42 and provide automated mental health assessment. However, these classical models face challenges in capturing complex emotional patterns and high-dimensional feature interactions, which limits their overall diagnostic efficiency. To overcome these limitations, Quantum Machine Learning (QML) was introduced. In this article, we applied the Variational Quantum Classifier (VQC) model that leverages quantum properties such as superposition and entanglement, allowing it to process and distinguish subtle mental health indicators more effectively than ML and DL approaches. The Quantum model (VQC) outperformed all classical approaches, delivering an exceptional accuracy of 99.95%, with precision, recall, and F1-score values nearly reaching 1.0, indicating highly reliable classification consistency. A comparative evaluation revealed that the quantum model demonstrated superior classification capabilities, outperforming classical systems in consistency and predictive reliability. This work establishes QML as a promising direction toward next-generation mental health diagnosis systems.
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.
Cardiovascular disease remains one of the leading causes of death worldwide, making early and accurate risk prediction essential. Conventional machine learning models often struggle to handle complex medical data, resulting in limited accuracy and reduced interpretability. To address these challenges, this study presents a hybrid prediction framework that combines Quantum Machine Learning (QML) with ensemble learning techniques. Classical models such as Support Vector Classifier (SVC) and Artificial Neural Network (ANN) are used as baseline approaches. In addition, quantum models including QSVC, Variational Quantum Classifier (VQC), and Quantum Neural Network (QNN) are employed to capture complex non-linear patterns present in clinical data. To further enhance performance, a Bagging-QSVC ensemble model is proposed, improving robustness and overall classification accuracy. Interpretability is incorporated using SHAP, allowing the model’s predictions to be more transparent and easier to understand in a clinical context. Experimental results indicate that the proposed quantum ensemble achieves an accuracy of around 90%, outperforming both classical models and individual quantum models. Furthermore, a real-time user interface is developed, enabling clinicians to input patient data and receive immediate risk predictions. Overall, the system provides a reliable and interpretable solution suitable for practical clinical decision support.
Artificial Intelligence (AI) systems have shown good success at classifying. However, the lack of explainability is a true and significant challenge, especially in high-stakes domains, such as health and finance, where understanding is paramount. We propose a new solution to this challenge: an explainable AI framework based on our comparative study with Quantum Boltzmann Machines (QBMs) and Classical Boltzmann Machines (CBMs). We leverage principles of quantum computing within classical machine learning to provide substantive transparency around decision-making. The design involves training both models on a binarised and dimensionally reduced MNIST dataset, where Principal Component Analysis (PCA) is applied for preprocessing. For interpretability, we employ gradient-based saliency maps in QBMs and SHAP (SHapley Additive exPlanations) in CBMs to evaluate feature attributions.QBMs deploy hybrid quantum-classical circuits with strongly entangling layers, allowing for richer latent representations, whereas CBMs serve as a classical baseline that utilises contrastive divergence. Along the way, we found that QBMs outperformed CBMs on classification accuracy (83.5% vs. 54%) and had more concentrated distributions in feature attributions as quantified by entropy (1.27 vs. 1.39). In other words, QBMs not only produced better predictive performance than CBMs, but they also provided clearer identification of "active ingredient" or the most important features behind model predictions. To conclude, our results illustrate that quantum-classical hybrid models can display improvements in both accuracy and interpretability, which leads us toward more trustworthy and explainable AI systems.