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.
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.
Brain imaging is a critical tool in mental health diagnostics, particularly for detecting anomalies associated with behavioral disorders, anxiety, depression, and neuropsychiatric conditions. Traditional deep learning approaches, such as Convolutional Neural Networks (CNNs), have shown promise in analyzing functional Magnetic Resonance Imaging (fMRI), but they often suffer from computational limitations, feature extraction inefficiencies, and generalization issues. Quantum Machine Learning (QML) offers a transformative solution by enhancing feature representation, improving classification accuracy, and exploiting the power of quantum computation for large-scale medical imaging datasets. In this paper, we explored the potential of quantum models for detecting anomalies in brain imaging. Using the UCLA Consortium for Neuropsychiatric Phenomics dataset, we compared quantum models, including the Variational Quantum Classifier (VQC), Quantum Convolutional Neural Network (QCNN), and Quantum Support Vector Machine (QSVM), with classical models like CNNs and the Support Vector Machine (SVM). Our results revealed that QSVM and VQC outperformed classical models, both achieving 92% accuracy and perfect recall (100%), making them highly effective at identifying positive cases-a critical requirement in clinical diagnostics. QCNN, while less accurate at 74%, still showed strong precision (90%) and an F1score of 85%, highlighting its potential where avoiding false positives is key. Classical models, in comparison, performed moderately well: SVM reached 87% accuracy and CNN achieved 83%, but both had lower precision and F 1 -scores, suggesting less consistent performance. These findings suggest that quantum models, especially VQC and QSVM, offer a promising advantage in neuroimaging-based mental health diagnostics.
Quantum machine learning (QML) offers a promising framework for analyzing complex, high-dimensional biomedical data, yet rigorous benchmarking on real-world clinical datasets remains limited. In this study, we evaluate a quantum support vector machine (QSVM) for agitation detection using the Technology Integrated Health Management (TIHM) smart-home dementia dataset. The proposed QSVM uses a 13-qubit fidelity-based feature map with two degrees of freedom in angle encoding to process the same 26 features used by the strongest classical baselines under a subject-independent evaluation protocol. Across seven ID-disjoint trials, the QSVM achieves a weighted F1-score of0.92±0.06, with consistently high precision of0.93±0.06and recall of0.91±0.06. These results demonstrate that quantum kernel methods can match or exceed state-of-the-art classical performance while providing a reproducible benchmark for cognitive-health prediction. This work lays the foundation for future exploration of hybrid quantum–classical models and clinical decision-support applications in behavioral health monitoring.
Mental health disorders represent a growing global healthcare challenge affecting hundreds of millions of people worldwide. Major depressive disorder (MDD), anxiety disorders, post-traumatic stress disorder (PTSD), and Alzheimer’s disease place huge pressure on medical systems. A central challenge is early detection, which remains difficult due to the complexity of symptoms related to these diseases. Delayed diagnosis is also due to limited access to specialists and the multimodal complexity of relevant biomarkers, clinical assessments, neuroimaging, physiological signals, and behavioral data. Recent developments in artificial intelligence (AI) have demonstrated promising results in improving diagnostic support. However, classical AI still struggles to model high-dimensional correlations and inherent uncertainties that are common to cognitive-emotional data. This chapter explores quantum machine learning (QML) as a transformative paradigm to overcome these limitations. We provide a comprehensive overview of foundational QML architectures and evaluate its suitability for processing diverse healthcare data such as neuroimaging, wearable sensor inputs, and electronic health records (EHRs). We propose a new conceptual framework for a quantum-enabled clinical decision support system that integrates multimodal feature extraction. Taking into consideration the sensitivity of psychiatric data, we critically examine security imperatives that include federated learning and post-quantum privacy protection. This chapter aims to provide researchers and practitioners with a comprehensive perspective on how quantum computing (QC) may contribute to future intelligent mental 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.
Early and accurate anomaly detection in Intensive Care Unit (ICU) monitoring is vital for improving patient outcomes and reducing mortality. Traditional machine learning techniques often face challenges in handling the noisy, high-dimensional, and limited datasets characteristic of ICU physiological signals. In this study, we present an early investigation of a quantum-enhanced framework employing a classically simulated Quantum Support Vector Machine (QSVM) to improve the detection of critical anomalies in real-world ICU time-series data. Utilizing the publicly available PhysioNet 2012 Challenge dataset, we extract comprehensive statistical and temporal features from vital signs including heart rate, blood pressure, respiratory rate, and oxygen saturation. Our QSVM model leverages a quantum kernel mapped via Qiskit’s ZZFeatureMap and is rigorously benchmarked against classical machine learning classifiers such as Support Vector Machines with radial basis function kernels, Random Forest, and XGBoost. Experimental results demonstrate that the QSVM shows competitive and robust classification performance, particularly excelling in scenarios with limited and noisy data, with an AUC of 0.92, particularly excelling in scenarios with limited and noisy data. Furthermore, the quantum kernel’s ability to implicitly project data into a high-dimensional Hilbert space provides enhanced separability of complex physiological patterns. This work constitutes one of the first simulation-based applications of quantum machine learning to critical care anomaly detection, underscoring the promise of hybrid quantum-classical approaches for advancing real-time clinical decision support systems.
Mental health has a deep effect on human health affecting individuals in physically, emotionally and socially. Poor mental health can lead to numerous physical, emotional and behavioral consequences which can significantly impact overall health and quality of life. So, Mental health disorders (MHD) prediction is essential in the field of research for early identification and intervention, improving treatment efficacy, reducing stigma, improving quality of life, addressing Comorbidities, etc. The Mental Health Disorders (MHD) can be tracked using a range of methods including: Quantum Computing (QC), Federated Learning (FL), Machine Learning (ML), Deep Learning (DL), and Edge Computing. In this study we focus on the use of Quantum ML (QML) to predict MHD as Anxiety (AX), Depression (DP), Loneliness (LL), Stress (ST), or Normal (NR). For this study we will evaluate three models: Quantum Gradient Boosting (QGB); Quantum Support Vector Machine (QSM); and Hybrid Quantum Classical Model (HQM). Each model will be compared based on Accuracy (A in %); Precision (P in %); Recall (R in %); F1 Score(F1S in %); Training time (TT in secs); Memory Usage (M in MB); & Model Size (MS in KB) with 80:20, 70:30, & 60:40 Training Testing Ratios (TTR in \%) before and after applying Grid Search (GS) based Hyperparameter (HP) Tuning (HPT) optimization to identify if there is a statistically significant improvement in performance. Additionally, the receiver operating characteristic (ROC) curve will provide TPR & FPR for each model; while Boxplot & Heatmaps will visually represent these parameters. Analysis of results show that the performance of HQM is higher on average (A, P, R, F1S) than A, P and TT with averages of 94.1, 94.0, 94.1, 94.2 and 4.49 for both HQM before and HQM after HPT testing. However, QSM performs better in terms of M and MS with values 1274.41 and 88.54, and 1269.36 and 80.53 accordingly.