Prolonged stress can negatively impact a person's mental health leading to multiple diseases. Stress monitoring can be efficiently done with the help of artificial intelligence technology combined with the benefits of quantum computing. The main aim of the paper is to analyze quantum enhanced machine learning techniques in predicting the stress of knowledge workers at office through multiple modalities. A general overview of popular quantum enhanced machine learning methods such as Quantum Support Vector Machine (QSVM), Variational Quantum Classifier (VQC) and Quantum K-Nearest Neighbor (QKNN) methods has been presented after studying the literatures of past 10 years. Besides, these models have been implemented on multimodal SWELL-KW dataset, which contains knowledge worker's computer interaction, facial expressions, body postures, heart rate variability and skin conductance data recorded in various working conditions. Further, the impacts of Quantum Principal Component Analysis based feature reduction on their performances have been analyzed. Experimental results show that for the current dataset, QSVM model with PCA on heart rate variability and skin conductance data results in highest accuracy of 0.8.
Prolonged stress can negatively impact a person's mental health leading to multiple diseases. Stress monitoring can be efficiently done with the help of artificial intelligence technology combined with the benefits of quantum computing. The main aim of the paper is to analyze quantum enhanced machine learning techniques in predicting the stress of knowledge workers at office through multiple modalities. A general overview of popular quantum enhanced machine learning methods such as Quantum Support Vector Machine (QSVM), Variational Quantum Classifier (VQC) and Quantum K-Nearest Neighbor (QKNN) methods has been presented after studying the literatures of past 10 years. Besides, these models have been implemented on multimodal SWELL-KW dataset, which contains knowledge worker's computer interaction, facial expressions, body postures, heart rate variability and skin conductance data recorded in various working conditions. Further, the impacts of Quantum Principal Component Analysis based feature reduction on their performances have been analyzed. Experimental results show that for the current dataset, QSVM model with PCA on heart rate variability and skin conductance data results in highest accuracy of 0.8.
Exposure to continuous stress can negatively impact a person's mental health. Stress monitoring plays a significant role to detect the stress level of an individual. The main goal of this work is to develop a quantum augmented deep learning model to support knowledge workers in the self management of their stress levels in the office. Quantum computing is a technological leap that is expected to provide us with the fastest computational power than today's classical processors. In this paper, a parameterized quantum circuit is augmented in the traditional Long Short Term memory (LSTM) model to enhance its learning capability and improve the predictions. The effectiveness of the proposed method is evaluated on various sensor data obtained from SWELL-KW [1] dataset. The time series dataset contains knowledge worker's computer interaction, facial expression, body postures and heart rate (variability) and skin conductance data recorded in varied working conditions. With improved learning ability, the proposed model has shown promising prediction ability on data obtained from all the modalities. For dataset in hand, the model with heart rate variability data resulted in maximum F1-score of 87.67%.
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
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 electrocardiogram (ECG) is the most common technique used to diagnose heart diseases. The electrical signals produced by the heart are recorded by chest electrodes and by the extremity electrodes placed on the limbs. Many diseases, such as arrhythmia, cardiomyopathy, coronary heart disease, and heart failure, can be diagnosed by examining ECG signals. The interpretation of these signals by experts may take a long time, and there may be differences between expert interpretations. Since technological developments are intertwined with the medical sciences, computer-assisted diagnostic methods have recently come forward. In computer science, machine learning techniques are often preferred for automatic detection. Quantum-based structures have emerged to increase the machine learning algorithm's speed and classification performance. In this study, a quantum-based machine learning algorithm is applied to classify heart rhythms. The ECG properties were converted to qubit structure using principal component analysis (PCA). The resulting qubits are classified using the quantum support vector machine (QSVM) algorithm. Quantum computer simulation over Qiskit was used for classification studies. Within the scope of experimental studies, comparisons between classical SVM and QSVM were made using different data amounts and qubit numbers. In the results of the analysis, classical SVM achieved 86.96% accuracy, and QSVM achieved 84.64% accuracy. Despite the fact that the entire dataset was not used due to various limitations, these successful performances were achieved. Classification of medical data such as that from ECG has shown that quantum-based machine learning frameworks perform well despite current resource constraints. In this respect, the study includes essential contributions to the use of quantum-based machine learning methods on signal data in medicine.
Background Artificial Intelligence (AI) incorporation within healthcare applications has significantly helped in the improvement of healthcare diagnostics, patient forecasting, and clinical decision-making. The steady growing of medical data in terms of quantity, quality, and dimension poses more significant problems for traditional AI methods in terms of size, time, and computational efficiency. However, classical machine learning models often struggle with high-dimensional, imbalanced, and heterogeneous medical datasets, which limits their scalability and generalizability. The Quantum Artificial Intelligence (QAI) model, which combines quantum computing and machine learning in a complementary way, has emerged as one of the main trends to defeat these problems. This complementary approach is gaining more popularity, with the most advanced work being developed in this area, as it offers a fundamental explanation of current complexities and the underlying principles and their applications. Methodology This paper begins with the basic concepts of quantum computing and where it overlaps with AI. It then moves to a comparative approach, discussing various quantum machine learning algorithms such as quantum support vector machines, quantum neural networks, and variational quantum circuits. A comparative evaluation of quantum support vector machine, variational quantum classifier, quantum neural network, quantum K-means, and quantum Boltzmann machine is conducted using the Pima Indians Diabetes dataset to assess model behaviour and performance under standard metrics. Further detail is provided on the theoretical and practical implications of QAI in medical imaging, genomics, electronic health records, and drug discovery. Additionally, this paper examines platforms, tools, and quantum hardware used for QAI research, and discusses unique challenges currently faced in quantum technology. Results Unlike prior surveys that primarily provide conceptual overviews, this paper uniquely combines a comprehensive review with practical benchmarking of multiple quantum machine learning models on a benchmark medical dataset. This provides comparative insights into algorithmic performance under real-world constraints, particularly in relation to high-dimensional medical data, genomic data, electronic health records with large number of clinical attributes, radiology images, etc. In this study, dimensionality arises from multivariate clinical features encoded into quantum feature maps and the limitations of current quantum systems. Conclusion This paper aims to serve as a starting point for researchers and practitioners interested in the potential of quantum-enhanced AI to transform health data analytics. By identifying current limitations, evaluating the performance of emerging quantum machine learning approaches, and outlining gaps in the literature, the paper highlights future research directions necessary to advance QAI in healthcare. Overall, the findings outline both the potential and practical constraints of quantum models, offering a foundation for future work toward scalable, clinically applicable QAI solutions.
To forecast heart problems, an innovative hybrid technique combining quantum computing with machine learning approaches is presented. For the preprocessing stage, normalization and dimensionality reduction techniques are used on a dataset of 270 patients' records and 13 clinical variables to make them appropriate for a 4-qubit quantum computer. Some traditional machine learning algorithms, including convolutional neural networks (CNN), support vector machines (SVM), and XGBoost, are trained to establish baselines in relation to other methods. Under the guidance of the SPSA algorithm, a quantum-inspired classifier called VQC is developed using a parameterized quantum circuit that includes rotation and entanglement operations. A quantitative study of crucial performance metrics reveals that the suggested quantum technique outperforms conventional methods in tests. The VQC classifier has the highest accuracy (85.2%), precision (86.4%), and F1 score (82.6%), while matching the SVM classifier's ROC-AUC score of 88.9%. Although with lower dimensionality, this research shows how quantum-based machine learning may find complex correlations among variables. This study is intended to serve as a preliminary concept for using quantum machine learning for healthcare applications, especially for increasing the accuracy rate and lowering misdiagnosis errors.