Prediction of Covid-19 and post Covid-19 patients with reduced feature extraction using Machine Learning Techniques

Corona virus has spread the Covid-19 pandemic to the whole world resulting in the loss of about 3.8 million people. Nearly 156.5 million people have recovered from this disease by timely diagnostic using primary symptoms, which include lethargy caused by muscular weakness. Post Covid-19 patients also face myalgia, which is caused by the abnormal neural action potential. Electromyography (EMG) has been used for years to detect the neural communication and the action potential caused by it. Biomedical experts prefer EMG over other methods due to its ability to capture and conserve the data which helps in detecting major muscular disorders. This paper depicts multiple approaches to diagnose current Covid-19 patients or post Covid-19 patients using the EMG data of lower limb using Machine Learning. These approaches vary from each other in the form of the information conserved in the training data. The proposed method achieves the highest accuracy of 93.8% along with increasing the computational efficiency, as compared to the conventional methods. The dataset used is a publically available dataset, provided by University of California, by the name of Irvine (UCI) EMG lower limb dataset.

Mental Health Issues in Female Population during Covid-19 Pandemic

Background: Female population can be affected by various psychological factors that can have adverse effects on the woman's mental health. Pandemics are one such times which can have negative effect on the mental health. Therefore, this study was conducted to determine the status of depression and anxiety and factors associated with it in the female population (pregnant and non-pregnant women) during the outbreak of COVID-19. Methods: This descriptive-analytical cross-sectional study was performed on 345 women coming to a tertiary care hospital in Karachi, Pakistan. The data was collected using the socio-demographic characteristics questionnaire and the GAD-7 and PHQ-9 questionnaires. Regression analysis was done to determine the association between various factors with depression and anxiety. Results: Mean age of the participants was 27.9 years Depression and anxiety symptoms were observed in 32.7, 32.7, and 43.9% of the participants, respectively The mean score of depression was found to be 3.72 (3.80) whereas of anxiety was 3.5 (4.006). The overall prevalence of depression was 30.5% and that of anxiety was 18.55%, with varying degrees from mild to severe. Conclusions: Females in general and pregnant women specifically need special attention of the health care policy makers and this group should be given importance especially in times of such pandemics and all necessary measures should be taken to provide mental and psychological support.