Chronic Fatigue Syndrome (CFS) is a disorder with complex symptoms among patients. In most cases, CFS sufferers describe severe body weakness, poor sleep and inability to perform their usual work as their primary complaints. Symptoms worsen when the patient attempts to do similar work as tolerated. To prevent the worsening of symptoms, the patients need to be aware of what intensity of work they can manage. In this paper, we propose CFSCare, a hardware and software-based system that uses ML models to measure CFS patients' daily activity and energy expenditure objectively. Through our developed App, CFSCare submits to the user a summary of the comprehensive reports of the user's activity and sends a recommendation to the user on how they can prevent acquiring symptoms of CFS brought about by over-exertion. We use an Android smartphone and wrist sensor (MetamotionC) to monitor their leg and hand activity. We develop ML models based on SVM and DT algorithms to predict particular leg and hand activities. Among the applied ML models, SVM exhibited a brilliant performance with 98% accuracy in predicting leg activities and an average to-fold cross-validation score of 94%. For the hand activities prediction, DT recorded the best accuracy of 96%, and the average score of to cross-validations is also 96%. Since CFS patients can tire with exertion after a small amount of daily activity, CFSCare can playa vital role in preventing the patient from over-exertion through the monitoring features.

The aim of this study is to enhance the accuracy and reduce the time complexity of predicting cardiac illnesses by utilizing patient vital signs exclusively. The importance of timely treatment in critical situations where quick decisions are necessary is emphasized here. Precise predictions have the potential to prevent health deterioration and even save lives. By analyzing vast datasets (images, texts etc.) and detecting subtle patterns, quantum machine learning algorithms (QML) can offer more accurate results in a shorter period compared to classical algorithms. The feature set used here is a novel one to be used for the quick detection and early prediction of cardiovascular diseases, and also a convenient one for this task. To predict heart diseases or abnormalities, both classical and quantum machine learning techniques have been employed in this paper. We have developed four models based on Support Vector Machine (SVM), Neural Network (NN), Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) and compared their performance on a balanced sample of a vast dataset for heart diseases prediction. After comparing the models' performance, we found that Quantum Support Vector Machine (QSVM) performed best with an accuracy of 75% and to-fold Cross-validation score of 80%.

Quantum machine learning (QML) in the field of disease detection and prediction use quantum computing techniques and algorithms to analyze and classify large datasets of medical information, by identifying subtle patterns and predict the occurrence or progression of diseases. It involves applying machine learning techniques to data from biological and medical research, such as-genomic and proteomic data, medical imaging, electronic health records, and clinical trial data, using quantum computing algorithms and architectures to perform these analyses more efficiently and accurately than classical computing methods. This approach has the potential to provide new insights into complex biological systems and facilitate the development of more effective treatments and personalized medicine. In this paper, a systematic review of the use of QML algorithms has been conducted, which focuses on the detection and prediction of diseases among patients. The current essence of the field along with the challenges and limitations of current works have also been discussed. After evaluating the implemented and proposed methods of data analysis, algorithm development, usefulness and efficiency of the system in various disease detection and prediction, a recommendation was made on the open research scopes in this field at the end of the paper.

Heart Rate (HR) and Heart Rate Variability (HRV) is an essential measurement to know the heart’s cardiovascular condition. Many works have been done for measuring HR-HRV based on the facial video non-invasively. In this paper, based on our previous work experience of measuring HR-HRV by Remote photoplethysmography signals (rPPG) analysis, we have built a prediction model from the 10-second time series data extracted from a facial video. In this work, we have used the instantaneous public dataset with several data models to predict the HR-HRV, and stress levels exclusively from the dataset. We have used here some of the popular algorithms appropriate for this task. We have also analyzed the stress level classification on the gender of a subject using the same facial videos with 16 different classifiers resulting in almost perfect accuracy for several classifiers.

In the ongoing pandemic of COVID-19, the entire population of the world is getting affected either physically or mentally. In terms of physical diseases, the symptoms and treatments are more known, and people tend to be more aware of them. But the mental health is frequently ignored by general people, which has worsened during the pandemic. Though several research were conducted to cope with mental health analysis, detection and treatment in this current pandemic situation, the practical implementation are few. Among them the optimal results are produced by the ones adapting artificial intelligence (AI). Also, remote healthcare services have provided their supportive hands to combat the situation. In this paper, a systematic review of the use of AI and remote healthcare services has been conducted, which focuses on the detection, analysis and treatment of mental health among people during the COVID-19 pandemic. The current essence, challenges and limitations have also been discussed here. After evaluating the methods of data collection, usefulness and efficiency of the available works in symptom detection remotely and correctly, and data analysis algorithms used by them- a recommendation was made on the open research scopes in this field.

Accurate and valid health information is crucial for effective medical management. Failure to collect adequate information from physical and mental health examinations can be a barrier to Virtual medical platforms and telemedicine. In this paper, we propose the non-invasive “Dream” project prototype to monitor and record heart rate (HR), heart rate-variation (HRV) (for physical health), and stress (for mental health) using only a smart-phone. This non-invasive mobile application, “Dream” uses the front camera to capture video to calculate HR-HRV and stress. The full “Dream” project encompasses our previous facial video HR-HRV and stress work. We have also compared our proposed “Dream” project with 39 works in this area. We found a significant positive difference between our proposed “Dream” project compared to other projects in respect to user accessibility, application, cost-effectiveness, hospitalization monitoring, and human health status. Furthermore, we can apply “Dream” in remote human health monitoring, driver monitoring, and creating vital sign records non-invasively without a health care assistant. Especially during a pandemic, this virtual health monitoring system can be useful for scaled-up telemedicine to serve the remote population.