The increasing prevalences of sleep disorders such as insomnia and sleep apnea has become most significant global health concern. Conventional machine learning (ML) models often struggle to deliver very high predictive accuracy or to capture complex and non-linear relationships embedded within multifactorial sleep and lifestyle datasets. This study explores a novel solution by applying the Quantum Machine Learning techniques, specifically the Quantum Support Vector Machine (QSVM) and the Variational Quantum Classifier (VQC), to the Sleep Health and Lifestyle Dataset. The dataset incorporates variables such as sleep duration, physical activity level, stress level, and cardiovascular metrics. Results reveal a clear quantum advantage, with overall accuracy improved by +0.0827 (an 8.27% increase), as the QSVM achieved 0.8100 compared to the traditional classical SVM’s 0.7273. Notably, the QSVM demonstrated superior performance in classifying ambiguous cases, raising the F1-score for the "None" class by 15.4%. These findings highlight Quantum Machine Learning as a robust, data driven avenues for enhancing early diagnosis and personalised intervention strategie.
The increasing prevalences of sleep disorders such as insomnia and sleep apnea has become most significant global health concern. Conventional machine learning (ML) models often struggle to deliver very high predictive accuracy or to capture complex and non-linear relationships embedded within multifactorial sleep and lifestyle datasets. This study explores a novel solution by applying the Quantum Machine Learning techniques, specifically the Quantum Support Vector Machine (QSVM) and the Variational Quantum Classifier (VQC), to the Sleep Health and Lifestyle Dataset. The dataset incorporates variables such as sleep duration, physical activity level, stress level, and cardiovascular metrics. Results reveal a clear quantum advantage, with overall accuracy improved by +0.0827 (an 8.27% increase), as the QSVM achieved 0.8100 compared to the traditional classical SVM’s 0.7273. Notably, the QSVM demonstrated superior performance in classifying ambiguous cases, raising the F1-score for the "None" class by 15.4%. These findings highlight Quantum Machine Learning as a robust, data driven avenues for enhancing early diagnosis and personalised intervention strategie.
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.
Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide, where early and accurate diagnosis plays a critical role in improving patient outcomes. While classical machine learning methods have shown strong performance in PD classification using clinical and acoustic data, they remain limited in capturing complex nonlinear feature interactions in high-dimensional spaces. This study explores the application of quantum machine learning (QML) techniques for PD prediction, focusing on Variational Quantum Classifiers (VQC) and Quantum Support Vector Machines (QSVM), and compares their performance with strong classical baselines including Logistic Regression, SVM, Random Forest, Grid Search–optimized models, and XGBoost. Experiments were conducted using stratified train–test splits and validated through cross-validation and statistical testing. Results show that classical models achieve competitive performance, with XGBoost reaching 91.9% accuracy. However, quantum models demonstrate slightly improved performance, with the VQC using EfficientSU2 achieving the highest accuracy of 93.1%, followed by the Real Amplitudes VQC (92.6%) and QSVM (92.2%). These improvements are consistent across evaluation metrics including F1-score and recall. Statistical analysis using 10-fold cross-validation indicates that the VQC (EfficientSU2) shows a significant improvement over classical baselines ( p = 0.031), while other quantum models do not achieve statistical significance over XGBoost ( p > 0.05). McNemar’s test on the independent test set confirms no significant difference in final prediction distributions between quantum and classical models. Overall, the findings suggest that quantum machine learning models are competitive with state-of-the-art classical approaches, offering marginal but consistent performance gains. This study highlights the potential of quantum-enhanced learning frameworks for biomedical classification tasks, while also emphasizing that their advantages remain incremental rather than definitive under current simulation-based implementations.
Heart disease is one of the major global causes of death, requiring early and precise detection for efficient treatment.With advancements in quantum computing, Variational Quantum Circuits (VQCs) have gained recognition as a valuable technique for improving learning models in medical diagnosis.This study further explores the application of VQC-based models for heart disease prediction, leveraging quantum computing to enhance classification accuracy.A hybrid quantum-classical model has been developed, which combines classical preprocessing for feature extraction from patient datasets with a VQC-based classifier for medical predictions.The quantum circuit using variational parameters is optimized to reduce the loss function and facilitate effective learning from complex medical data.This proposed approach is tested on benchmark heart disease datasets and evaluated against classical Machine Learning (ML) models, including Support Vector Machines (SVMs), Random Forests, and Neural Networks.The experimental results show that VQC-based models achieve high accuracy and outperform classical models, notably on high-dimensional, non-linearly separable data.The quantum advantage of VQCs lies in their ability to encode and process data in a huge Hilbert space.However, challenges such as noise in near-term quantum devices and scalability issues remain key.This research underscores the efficacy of VQCbased quantum machine learning in medical diagnosis, paving the way for future studies on quantum-enhanced healthcare solutions.Further advancements in quantum hardware and hybrid learning techniques could allow quantum-assisted heart disease prediction to become an effective clinical tool.
Accurate disease diagnosis is becoming a critical challenge owing to the limited success of classical machine-learning approaches, especially in classification problems. Accurate and rapid brain tumor classification in medical diagnosis could aid in early detection and treatment planning. This paper presents a novel quantum-enhanced machine learning approach using a Quantum Support Vector Machine (QSVM) and Variational Quantum Classifier (VQC) models for improved tumor classification. ZFeatureMap is deployed for the conversion of classical data points to quantum space, as it is found to be most suitable for the characteristics of the brain tumor dataset and provides a balance between accuracy, efficiency, and computational feasibility. The QSVM and VQC models are implemented on a publicly available brain tumor dataset, demonstrating significant improvement over classical algorithms. Both models were implemented on a 32-qubit quantum simulator and a seven-qubit quantum computer. The simulation results indicate that the QSVM-based model is 3.62% more accurate and 60.4 times faster, while the VQC-based model is 2.49% more accurate and twice as fast as classical SVM. The findings suggest that quantum-enhanced machine learning has the scope to revolutionize medical diagnosis with more precise and faster results.
Chronic stress may have detrimental impacts on mental health as well as an elevated risk of various diseases. Stress monitoring might become simple with the help of artificial intelligence and quantum computing. The main goal of this work is to investigate how quantum enhanced machine learning algorithms can be used to forecast the stress levels of knowledge workers in the workplace using many modalities. This paper provides a high-level overview of the most well-known quantum augmented machine learning algorithms., such as Variational Quantum Classifier (VQC)., Quantum K-Nearest Neighbor (QKNN) and Quantum Support Vector Machine (QSVM)., after analysing the literature during the last ten years
Ovarian cancer remains one of the leading causes of cancer-related mortality among women worldwide, largely because most cases are diagnosed at advanced stages. Current diagnostic tools and classical machine learning models often show limited sensitivity and specificity, particularly when applied to the complex, high-dimensional datasets required for accurate prediction. These limitations highlight the need for more powerful computational approaches capable of extracting subtle diagnostic patterns. The aim of this study is to enhance the accuracy and efficiency of ovarian cancer diagnosis by using quantum-inspired machine learning approach such as Quantum Support Vector Machines (QSVMs). The study utilized QSVMs to analyse clinical and genomic datasets, leveraging quantum mechanics principles like superposition and entanglement to process data more effectively than traditional machine learning models. The QSVM model was developed, trained, and evaluated using performance metrics such as accuracy, sensitivity, specificity, and processing efficiency. The results demonstrated that QSVMs achieved a diagnostic accuracy of 92%, outperforming traditional support vector machines and other classical models. Additionally, the processing time for diagnosis was reduced from 45 minutes to 20 minutes, providing a faster and more reliable workflow. The QSVMs also excelled in analysing multi-omics data, enabling the identification of early-stage ovarian cancer biomarkers and supporting personalized treatment strategies. In conclusion, this study demonstrates the potential of QSVMs to transform ovarian cancer diagnostics by addressing key limitations of conventional methods. The findings underscore the importance of adopting quantum-inspired machine learning in medical applications and encourage further exploration of these advanced algorithms in improving healthcare outcomes.