Public Opinion Analysis for the Covid-19 Pandemic Based on Sina Weibo Data

The outbreak of Covid-19 has been continuously affecting human lives and communities around the world in many ways. In order to effectively prevent and control the Covid-19 pandemic, public opinion is analyzed based on Sina Weibo data in this paper. Firstly the Weibo data was crawled from Sina website to be the experimental dataset. After preprocessing operations of data cleaning, word segmentation and stop words removal, Term Frequency Inverse Document Frequency (TF-IDF) method was used to perform feature extraction and vectorization. Then public opinion for the Covid-19 pandemic was analyzed, which included word cloud analysis based on text visualization, topic mining based on Latent Dirichlet Allocation (LDA) and sentiment analysis based on Naïve Bayes. The experimental results show that public opinion analysis based on Sina Weibo data can provide effective data support for prevention and control of the Covid-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Severity Classification of COVID-19 Patients Data using Quantum Machine Learning Approaches

Artificial intelligence (AI) is assisting in several aspects of the COVID-19 pandemic, including medical diagnosis and therapy, drug development, molecular research and epidemiology. The involvement of AI in healthcare can help doctors to detect symptoms more quickly. In such era, we use the Quantum Machine Learning (QML) approaches to address clinical applications of machine learning (ML) technology, such as electronic healthcare data and clinical features. This paper presents the two QML algorithms, i.e, Enhanced Quantum Support Vector Machine (E-QSVM) and Quantum Random Forest (QRF) applied to COVID-19 and influenza datasets, which were collected from different private hospitals. The experimental results show that the proposed models outperform in terms of accuracy by achieving the highest accuracy of 78% for the E-QSVM model and 75% for the QRF model respectively. The competency of the models is obtained by comparing them with classical models and recently published quantum models. © 2022 IEEE.

Reptile: Aggregation-level Explanations for Hierarchical Data

Users often can see from overview-level statistics that some results look "off", but are rarely able to characterize even the type of error. Reptile is an iterative human-in-the-loop explanation and cleaning system for errors in hierarchical data. Users specify an anomalous distributive aggregation result (a complaint), and Reptile recommends drill-down operations to help the user "zoom-in" on the underlying errors. Unlike prior explanation systems that intervene on raw records, Reptile intervenes by learning a group's expected statistics, and ranks drill-down sub-groups by how much the intervention fixes the complaint. This group-level formulation supports a wide range of error types (missing, duplicates, value errors) and uniquely leverages the distributive properties of the user complaint. Further, the learning-based intervention lets users provide domain expertise that Reptile learns from. In each drill-down iteration, Reptile must train a large number of predictive models. We thus extend factorised learning from countjoin queries to aggregation-join queries, and develop a suite of optimizations that leverage the data's hierarchical structure. These optimizations reduce runtimes by >6x compared to a Lapack-based implementation. When applied to real-world Covid-19 and African farmer survey data, Reptile correctly identifies 21/30 (vs 2 using existing explanation approaches) and 20/22 errors. Reptile has been deployed in Ethiopia and Zambia, and used to clean nationwide farmer survey data;the clean data has been used to design national drought insurance policies.

Analysis and Prognosis of Covid-19 using Machine Learning Algorithms and Visualization using Tableau

This paper uses prognosticative machine learning models that predict corona positives and deaths as a result of the crisis, and the recovery rate from the pandemic. This method aids in diagnosing the contours of an individual's presumption in data transmission based on medical knowledge and calculates the unfolding virus's socioeconomic impact. It examines the Covid-19's spread technique with the help of machine learning models. It also identifies the approaching prophecy and recessive presumption of the crisis at the same time, and as a result, this applicable analysis aids similar countries in making decisions. This paper also considers the global prevalence of the plague. Within the first phase of the irruption, eight supervised classification epidemiologic models are used to estimate the day-to-day and monomer incidents of coronavirus throughout the world, as well as the vital replica variety, growth rate, and increasing time. Calculations are also made for the more intricate efficacious replica variety, which reveals that since the predominant cases are confirmed to the specific countries, the severity has decreased. Machine learning models' prognosticative capabilities are found to provide an additional satisfactory match, and simple estimates of daily incidents around the world. © 2022 IEEE.

An innovative big data framework for exploring the impact on decision-making in the European Mediterranean healthcare sector

Purpose>The purpose of this paper is to propose a new conceptual framework for big data analytics (BDA) in the healthcare sector for the European Mediterranean region. The objective of this new conceptual framework is to improve the health conditions in a dynamic region characterized by the appearance of new diseases.Design/methodology/approach>This study presents a new conceptual framework that could be employed in the European Mediterranean healthcare sector. Practically, this study can enhance medical services, taking smart decisions based on accurate data for healthcare and, finally, reducing the medical treatment costs, thanks to data quality control.Findings>This research proposes a new conceptual framework for BDA in the healthcare sector that could be integrated in the European Mediterranean region. This framework introduces the big data quality (BDQ) module to filter and clean data that are provided from different European data sources. The BDQ module acts in a loop mode where bad data are redirected to their data source (e.g. European Centre for Disease Prevention and Control, university hospitals) to be corrected to improve the overall data quality in the proposed framework. Finally, clean data are directed to the BDA to take quick efficient decisions involving all the concerned stakeholders.Practical implications>This study proposes a new conceptual framework for executives in the healthcare sector to improve the decision-making process, decrease operational costs, enhance management performance and save human lives.Originality/value>This study focused on big data management and BDQ in the European Mediterranean healthcare sector as a broadly considered fundamental condition for the quality of medical services and conditions.

Strategies and Lessons Learned During Cleaning of Data From Research Panel Participants: Cross-sectional Web-Based Health Behavior Survey Study.

BACKGROUND: The use of web-based methods to collect population-based health behavior data has burgeoned over the past two decades. Researchers have used web-based platforms and research panels to study a myriad of topics. Data cleaning prior to statistical analysis of web-based survey data is an important step for data integrity. However, the data cleaning processes used by research teams are often not reported. OBJECTIVE: The objectives of this manuscript are to describe the use of a systematic approach to clean the data collected via a web-based platform from panelists and to share lessons learned with other research teams to promote high-quality data cleaning process improvements. METHODS: Data for this web-based survey study were collected from a research panel that is available for scientific and marketing research. Participants (N=4000) were panelists recruited either directly or through verified partners of the research panel, were aged 18 to 45 years, were living in the United States, had proficiency in the English language, and had access to the internet. Eligible participants completed a health behavior survey via Qualtrics. Informed by recommendations from the literature, our interdisciplinary research team developed and implemented a systematic and sequential plan to inform data cleaning processes. This included the following: (1) reviewing survey completion speed, (2) identifying consecutive responses, (3) identifying cases with contradictory responses, and (4) assessing the quality of open-ended responses. Implementation of these strategies is described in detail, and the Checklist for E-Survey Data Integrity is offered as a tool for other investigators. RESULTS: Data cleaning procedures resulted in the removal of 1278 out of 4000 (31.95%) response records, which failed one or more data quality checks. First, approximately one-sixth of records (n=648, 16.20%) were removed because respondents completed the survey unrealistically quickly (ie, <10 minutes). Next, 7.30% (n=292) of records were removed because they contained evidence of consecutive responses. A total of 4.68% (n=187) of records were subsequently removed due to instances of conflicting responses. Finally, a total of 3.78% (n=151) of records were removed due to poor-quality open-ended responses. Thus, after these data cleaning steps, the final sample contained 2722 responses, representing 68.05% of the original sample. CONCLUSIONS: Examining data integrity and promoting transparency of data cleaning reporting is imperative for web-based survey research. Ensuring a high quality of data both prior to and following data collection is important. Our systematic approach helped eliminate records flagged as being of questionable quality. Data cleaning and management procedures should be reported more frequently, and systematic approaches should be adopted as standards of good practice in this type of research.

A Review: Application of Machine Learning Algorithm in Medical Diagnosis

After Covid 19 Pandemic people are more focusing on healthcare. Every person wants to get the solution related to any health issue from their doorstep, this is the reason that Machine learning techniques has been adopted very fast in the field of medical diagnosis which can provide fast and accurate diagnosis results at the time of disease diagnosis step this system will assist physician to predict the diseases in early stage. Using Machine learning the correct diagnosis can be done when the system will get the complete, sufficient and proper information with respect to the problem. Because of if the system will not get the proper information related to the disease this will leads to some diagnostic error by this adverse impact on the treatment of the patient. Machine learning works upon the concept of train and test the machine with the required algorithm which can provide efficient result for execution of this process first we need to train the machine with respect to the data collected and after collecting the data, data cleaning processing to be done efficiently so that we get the correct feature extraction when we follow the test step. In this research paper we are presenting comparative analysis of various machine learning algorithm ie. Linear regression. Decision tree, SVM, Random Forest etc. Applied in the field of medical diagnosis our analysis in focusing on the criteria with respect to the accuracy, performance and algorithm is applied for medical diagnosis. © 2021 IEEE.