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COVID-19 preventive measures have been a hindrance to millions of people over the globe not only affecting their daily routine but also affecting the mental stability. Among several preventive measures for COVID-19 spread, the lockdown is an important measure which helps considerably reduce the number of cases. The updated news about the COVID-19 is drastically spread in social media. Particularly, Twitter is widely used to share posts and opinions about the COVID-19 pandemic. Sentiment analysis (SA) on tweets can be used to determine different emotions such as anger, disgust, sadness, joy, and trust. But transparence is needed to understand how a given sentiment is evaluated with the black-box machine learning models. With this motivation, this paper presents a new explainable artificial intelligence (XAI)-based hybrid approach to analyze the sentiments of the tweets during different COVID-19 lockdowns. The proposed model attempted to understand the public's emotions during the first, second, and third lockdowns in India by analyzing tweets on social media, and demonstrates the novelty of the work. A new hybrid model is derived by integrating surrogate model and local interpretable model-agnostic explanation (LIME) model to categorize and predict different human emotions. At the same time, the Topj Similarity evaluation metric is employed to determine the similarity between the original and surrogate models. Furthermore, top words using the feature importance are identified. Finally, the overall emotions during the first, second, and third lockdowns are also estimated. For validating the enhanced outcomes of the proposed method, a series of experimental analysis was performed on the IEEE port and Twitter API dataset. The simulation results highlighted the supremacy of the proposed model with higher average precision, recall, F-score, and accuracy of 95.69%, 96.80%, 95.04%, and 96.76%, respectively. The outcome of the study reported that the public initially had a negative feeling and then started experiencing positive emotions during the third lockdown. © 2022 World Scientific Publishing Company.
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Background: The importance of early diagnosis of a hazardous illness cannot be overstated. The transmission rate is extremely high, especially in the current pandemic condition. The ability to predict epidemics will aid public health in reducing mortality and morbidity. Machine Learning (ML) approaches are used in the construction of an effective disease prognosis model. Furthermore, only if the model learns good associated features from the data is it possible to generate a speedy outcome. As a result, selecting features is also necessary before beginning the forecasting process. Objective(s): However, because of the virus's dynamic structure, it's difficult to predict Nipah disease and/or zoonotic infection. Furthermore, there is no clinical treatment for Nipah. The major goal of this research is to develop a prognostic model for early diagnosis of Nipah disease using a combination of several clinical factors such as symptoms, disease incubation information, and routine blood test results confirmed by a lab technician.Proposed System: The healthcare application and data are more complex to handle than other ML applications since various clinical features are assessed throughout disease manifestation. As a result, selecting the most relevant variables is critical when designing a prognosis model for any viral disease. To deal with clinical features from a vast number of features, we proposed a Restricted Boltzmann Machine (RBM) method in this research. Additionally, we employed a hybrid ensemble learning method to predict if the patient was infected with NiV after choosing features using the RBM. Data Collection: The proposed system is being implemented using the NiV infection dataset that erupted in Kozhikode, Kerala in 2018 and 2019. Result(s): The developed stacking-based ensemble Meta classifier was successfully implemented using the python programming language, and its performance was evaluated using a variety of metrics includingaccuracy, precision, recall, f1-score, log loss, AUROC and MCC. Our proposed Stacking Ensemble Meta Classifier (SEMC) model achieved an accuracy rate of 88.3% with a log loss of 0.36. Model precision, recall, f1-score, AUROC, and MCC value were 92.5%, 89.2%, 90.9%, 92.1%, and 0.74 respectively. In addition, we calculated the gravitational pull of each feature using the SHAP approach and discovered that altered sensorium, fever, headache, and cough were the most critical clinical indicators that distinguished NiVD infection from our dataset. Therefore, this classification may assist the pathologist in diagnosing NiVD with symptoms before performing the RT-PCR medical test. Conclusion(s): Using our proposed SEMC technique, we developed a prognostic model for the diagnosis of Nipah in humans. The proposed technique's discriminatory efficiency exhibited good NiVD diagnosis efficacy. We anticipate that this model will aid medics in determining a prognosis more quickly during future epidemics. However, to achieve maximum accuracy, the model requires more unique samples.Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
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COVID-19 preventive measures have been a hindrance to millions of people over the globe not only affecting their daily routine but also affecting the mental stability. Among several preventive measures for COVID-19 spread, the lockdown is an important measure which helps considerably reduce the number of cases. The updated news about the COVID-19 is drastically spread in social media. Particularly, Twitter is widely used to share posts and opinions about the COVID-19 pandemic. Sentiment analysis (SA) on tweets can be used to determine different emotions such as anger, disgust, sadness, joy, and trust. But transparence is needed to understand how a given sentiment is evaluated with the black-box machine learning models. With this motivation, this paper presents a new explainable artificial intelligence (XAI)-based hybrid approach to analyze the sentiments of the tweets during different COVID-19 lockdowns. The proposed model attempted to understand the public's emotions during the first, second, and third lockdowns in India by analyzing tweets on social media, and demonstrates the novelty of the work. A new hybrid model is derived by integrating surrogate model and local interpretable model-agnostic explanation (LIME) model to categorize and predict different human emotions. At the same time, the TopjSimilarity evaluation metric is employed to determine the similarity between the original and surrogate models. Furthermore, top words using the feature importance are identified. Finally, the overall emotions during the first, second, and third lockdowns are also estimated. For validating the enhanced outcomes of the proposed method, a series of experimental analysis was performed on the IEEE port and Twitter API dataset. The simulation results highlighted the supremacy of the proposed model with higher average precision, recall, F-score, and accuracy of 95.69%, 96.80%, 95.04%, and 96.76%, respectively. The outcome of the study reported that the public initially had a negative feeling and then started experiencing positive emotions during the third lockdown. © 2022 World Scientific Publishing Company.
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Introduction: At the time when the world was unprepared for the corona pandemic, the health-care workers faced the challenge with great effort. Recently, the OMICRON has been burdening the medical fraternity. Hence, in our study, we aimed to evaluate the "knowledge, attitude, and practices" related to OMICRON among the medical health-care staff. Materials and Methods: We piloted an online cross-sectional questionnaire study using Google Forms among 1000 medical staff working at various levels of public and private sections. The questions were formulated to test for the practices, attitude, and knowledge of the new variant OMICRON. The data collected were compared using the Chi-square test, deliberating P < 0.05 as significant. Results: We observed that majority were male participants, with significant number at the ages of 20-40 years. There was lower knowledge, although good practice and positive attitude were noted among the medical health staff. Conclusion: The medical health-care personnel possessed less knowledge regarding the new variant OMICRON, although positive practices and attitudes were noted. Hence, the governments should take necessary steps to implement the training about the new variant.
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The main purpose of this topic is to provide an excellent classification method for predicting the disease based on the key aspect of the disease. Here, we used a multiclass variable database for the prediction;also the methods, random forest and linear SVC, are used for the classification. Furthermore, based on the confusion matrix, we can know the outcome of the prediction model. In this, all the results are discussed using the confusion matrix. Infectious diseases such as nCOVID-19 cause serious damage to the human body’s immune system. It recently emerged from China and affects neighbors’ country and flu-like symptoms initially manifest in 89.9%. The disease spreads faster than SARS-CoV and MERS-CoV, and soon, the disease begins to spread from one person to another, with high fever (101.4 F), inhalation or dyspnea, sore throat, sneezing and coughing. In India, as of January 31, 2020, the number of cases was one, and on March 28, 2020, the outrage began to rise to 909. In addition, COVID is also caused by pneumonia-related illnesses. So far, such epidemics have been studied and diagnosed by reverse transcriptase poly-merase chain reaction (RT-PCR) and serology laboratory testing. Chest X-ray or computed tomography helps identify damaged and white cells in the affected body, identifying pathogens, and the presence of abundant metagenomic sequence in RNA is a major clinical challenge. Since the vaccine has not yet been announced, the current treatment is supplemental care. In this study, we compared machine learning classification methods such as NN, SVM, MLP, RF and KNN, which are widely used in the healthcare sector to diagnose disease by X-ray. Doctors often prescribe chest radiography to diagnose and/or predict infections, since we have read numerous articles on coronavirus. Further, in the clinical perspective, machine learning plays a vital role in solving the problem of prognosis and, thanks to treatment monitoring, there are effective mechanisms. In the presence of airborne diseases, we need an effective tool such as machine learning to investigate this, because nCOVID is transmitted by sneezing or coughing and/or other pulmonary syndrome. Therefore, this review summarizes the current outbreaks of coronavirus and its closely related lung viruses such as influenza and pneumonia, medical-based machine learning (MML) techniques and comparative analysis of MML for infectious diseases. © Springer International Publishing AG 2018.
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Severe acute respiratory syndrome of coronavirus 2 (SARS-CoV-2) is a contagious deadly virus that had a significant impact on public health. It also causes a major impact on the lung complication with other comorbid disorders, such as cardiac and psychological conditions, which also leads to significant health and psychological burden to the patients. The restoration of the mental conditions among SARS-CoV-2 infected individuals becomes more essential. A 56-year-old female patient was admitted with chief complaints of dry cough and fever in the casualty care unit. She was diagnosed with positive SARS-CoV-2 infection and immediately admitted to covid care isolation wards. She was managed with antiviral drugs and other supportive care. Later, discharged with prednisone, oral anticoagulants, and vitamin supplements. Two weeks later she was presented with chief complaints of fatigue, palpitations, and dizziness diagnosed with Postural Orthostatic Tachycardia Syndrome (POTS) and anxiety disorder. Further, symptomatic management provided with a selective beta-blocker, anti-arrhythmic drugs, and anxiolytic drugs. The present case report provides a basic insight into complications of POTS and anxiety disorder associated with post-SARS-CoV-2 infection. The appropriate therapeutic treatment and providing better counselling may improve daily activity and quality of life in patients reported with POTS symptoms.
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Around 300 million people have Asthma throughout the world. Asthma is one of the very common health issues among adults and children. As per the present situation of covid-19, asthma patients are very much prone to be affected. Around 1000 to 1500 people die per day due to asthma. Deaths can be avoided if asthma patients are taken care of in the initial stages. Much care has to be taken especially in the present scenario. There is a need for continuous monitoring for asthma patients. A fog-based system for health care is proving to be the best for providing a high quality of monitoring and control of the disease. Here a framework that is based on the IoT(Internet of Things) is proposed for assessing the asthma levels in the patients and to avoid the risk of being hospitalized. Here the artificial neural network-based system is proposed which helps in predicting asthma as well as sending the alerts to the respective patient and the family concerns. And this achieves a high accuracy level of 86%. © 2021 IEEE.
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In early 2020, the corona virus disease (COVID-19) has become a global epidemic. The WHO announced the disease as a public health emergency of international importance (PHEIC), and the issue was considered a health emergency. Automated computed tomography (CD) detection of lung infections offers a tremendous opportunity to expand the traditional health approach to resolving COVID-19. But many problems with CT. Facing contaminated areas from fragments, which include greater variability in infectious properties and low-intensity comparison between infections and normal tissues. Moreover, by suppressing the project of an in-depth model, a lot of information cannot be collected over some time.