Smart Health System Using Stacking Ensemble Classification Algorithm

Data Mining is a powerful technology and is used to identify useful and understandable patterns by analyzing large sets of data. It gives a detailed view of various disease predictions. It will be more useful especially in pandemic times. During these days, doctors are in the front line and battling with the COVID-19 virus. It will be hard for people to immediately get medical guidance or appointments. Our proposed system, the smart health application will come in handy at these times. The system allows people to get medical guidance for their health issues. Also, the system is fed with symptoms and the disease-related with it which will give high accuracy for disease prediction. Our model aims to use Stacking Ensemble Classification Algorithms to give high accuracy and correct prediction than Naive Bayes, Random Forest, Support Vector Machine, K - Nearest Neighbor, Decision Tree, Logistic Regression for different types of 149 diseases. The GUI is designed which can be used easily to predict the different types of disease accurately.

A Deep Learning Based Face Mask and Social Distance Monitoring Framework for Covid-19

Covid-19 has become one of the most dangerous diseases suddenly which is infecting the people in all over the world. It has created an impact on the lives of thousands of people all over the world. Governments of various countries are trying to control the wide spread of COVID-19 in our society. It is a danger to the human existence, so it is highly important to stop spreading the disease as it is deadly contagious. Spreading of virus can be prevented, by maintaining social distance and hygiene. The major transmission mode of COVID-19 is through saliva and nose discharge.It is highly important to know the necessity of wearing mask in the public. We need an automatic monitoring system for time being to monitor the public so as to avoid the situation of spreading of the disease for not wearing mask and not maintaining social distance. We have applied a deep learning technique to check whether the person is having a face mask or not. This work is aimed to identify the face mask in the public places which helps in the reducing of spread of the virus. CNN is used for the model. The proposed model recognizes the face region in the image given as input and extracts the necessary facial features to identify the face mask region. © 2021 IEEE.

Cardiac inflammation in COVID-19: Lessons from heart failure.

Cardiovascular disease (CVD) is the most common co-morbidity associated with COVID-19 and the fatality rate in COVID-19 patients with CVD is higher compared to other comorbidities, such as hypertension and diabetes. Preliminary data suggest that COVID-19 may also cause or worsen cardiac injury in infected patients through multiple mechanisms such as 'cytokine storm', endotheliosis, thrombosis, lymphocytopenia etc. Autopsies of COVID-19 patients reveal an infiltration of inflammatory mononuclear cells in the myocardium, confirming the role of the immune system in mediating cardiovascular damage in response to COVID-19 infection and also suggesting potential causal mechanisms for the development of new cardiac pathologies and/or exacerbation of underlying CVDs in infected patients. In this review, we discuss the potential underlying molecular mechanisms that drive COVID-19-mediated cardiac damage, as well as the short term and expected long-term cardiovascular ramifications of COVID-19 infection in patients.

Are Our COVID Warriors Cared-for Enough?A Nationwide Survey on Stress Among Doctors During the COVID-19 Pandemic (preprint)

Limited and uneven accessibility to healthcare is a major impediment in the fight against the COVID-19 pandemic which continues on inexorably, across various parts of the globe. We conducted a nationwide survey of a large sample of Indian doctors to measure levels of perceived stress, identify risk factors for severe stress and assess their response to current issues related to safety and well-being of the HCP community. The survey found severely stressed doctors to be younger (<45years), of female gender working in the ICU setting and insecure regarding their finances. Concern regarding PPE shortages and ethical dilemmas of rationing care are factors inducing severe stress amongst doctors working in ICU settings. This is the first such survey done in the context of the COVID-19 pandemic from the Indian sub-continent. The findings have important implications on the International healthcare community, especially across Africa, Asia & South America where the contagion continues to wreak havoc. The survey has identified factors which adversely impact the mental health of doctors during this Pandemic. This can act as a valuable guide for governmental authorities, professional organisations and hospital managements to establish support systems at multiple levels for these COVID Warriors.