Cost of Living - Country Rankings Dataset

Context:

The "Cost of Living - Country Rankings Dataset" provides comprehensive information on the cost of living in various countries around the world. Understanding the cost of living is crucial for individuals, businesses, and policymakers alike, as it impacts decisions related to travel, relocation, investment, and economic analysis. This dataset is intended to serve as a valuable resource for researchers, data analysts, and anyone interested in exploring and comparing the cost of living across different nations.

Content:

This dataset comprises four primary columns:

1. Countries: This column contains the names of various countries included in the dataset. Each country is identified by its official name.

2. Cost of Living: The "Cost of Living" column represents the cost of living index or score for each country. This index is typically calculated by considering various factors, such as housing, food, transportation, healthcare, and other essential expenses. A higher index value indicates a higher cost of living in that particular country, while a lower value suggests a more affordable cost of living.

3. 2017 Global Rank: This column provides the global ranking of each country's cost of living in the year 2017. The ranking is based on the cost of living index mentioned earlier. A lower rank indicates a lower cost of living relative to other countries, while a higher rank suggests a higher cost of living position.

4. Available Data: The "Available Data" column indicates whether or not data for a specific country and year is available.

This dataset is designed to support various data analysis and visualization tasks. Users can explore trends in the cost of living, identify countries with high or low cost of living, and analyze how rankings have changed over time. Researchers can use this dataset to conduct in-depth studies on the factors influencing the cost of living in different regions and the economic implications of such variations.

Please note that the dataset includes information for the year 2017, and users are encouraged to consider this when interpreting the data, as economic conditions and the cost of living may have changed since then. Additionally, this dataset aims to provide a snapshot of cost of living rankings for countries in 2017 and may not cover every country in the world.

Link: https://www.theglobaleconomy.com/rankings/cost_of_living_wb/

Disclaimer: The accuracy and completeness of the data provided in this dataset are subject to the source from which it was obtained. Users are advised to cross-reference this data with authoritative sources and exercise discretion when making decisions based on it. The dataset creator and Kaggle assume no responsibility for any actions taken based on the information provided herein.

Electronic Health Legal Data

Exploring Laws and Regulations

By US Open Data Portal, data.gov [source]

About this dataset

This Electronic Health Information Legal Epidemiology dataset offers an extensive collection of legal and epidemiological data that can be used to understand the complexities of electronic health information. It contains a detailed balance of variables, including legal requirements, enforcement mechanisms, proprietary tools, access restrictions, privacy and security implications, data rights and responsibilities, user accounts and authentication systems. This powerful set provides researchers with real-world insights into the functioning of EHI law in order to assess its impact on patient safety and public health outcomes. With such data it is possible to gain a better understanding of current policies regarding the regulation of electronic health information as well as their potential for improvement in safeguarding patient confidentiality. Use this dataset to explore how these laws impact our healthcare system by exploring patterns across different groups over time or analyze changes leading up to new versions or updates. Make exciting discoveries with this comprehensive dataset!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Start by familiarizing yourself with the different columns of the dataset. Examine each column closely and look up any unfamiliar terminology to get a better understanding of what the columns are referencing.

• Once you understand the data and what it is intended to represent, think about how you might want to use it in your analysis. You may want to create a research question, or narrower focus for your project surrounding legal epidemiology of electronic health information that can be answered with this data set.

• After creating your research plan, begin manipulating and cleaning up the data as needed in order to prepare it for analysis or visualization as specified in your project plan or research question/model design steps you have outlined .

4 .Next, perform exploratory data analysis (EDA) on relevant subsets of data from specific countries if needed on specific subsets based on targets of interests (e.g gender). Filter out irrelevant information necessary for drawing meaningful insights; analyze patterns and trends observed in your filtered datasets ; compare areas which have differing rates e-health related rules and regulations tying decisions made by elected officials strongly driven by demographics , socioeconomics factors ,ideology etc.. . Look out for correlations using statistical information as needed throughout all stages in process from filtering out dis-informative subgroups from full population set til generating visualizations(graphs/ diagrams) depicting valid insight leveraging descriptive / predictive models properly validate against reference datasets when available always keep openness principal during gathering info especially when needs requires contact external sources such validating multiple sources work best provide strong seals establishing validity accuracy facts statement representing humans case scenarios digital support suitably localized supporting local languages culture respectively while keeping secure datasets private visible limited particular users duly authorized access 5 Finally create concrete summaries reporting discoveries create share findings preferably infographics showcasing evidence observances providing overall assessment main conclusions protocols developed so far broader community indirectly related interested professionals able benefit those results ideas complete transparently freely adapted locally ported increase overall global society level enhancing potentiality range impact derive conditions allowing wider adoption increased usage diffusion capture wide spread change movement affect global e-health legal domain clear manner

Research Ideas

• Studying how technology affects public health policies and practice - Using the data, researchers can look at the various types of legal regulations related to electronic health information to examine any relations between technology and public health decisions in certain areas or regions.
• Evaluating trends in legal epidemiology – With this data, policymakers can identify patterns that help measure the evolution of electronic health information regulations over time and investigate why such rules are changing within different states or countries.
• Analysing possible impacts on healthcare costs – Looking at changes in laws, regulations, and standards relate...

Global Data: GDP, Life Expectancy & More

This dataset comprises 204 entries and 38 attributes, providing a comprehensive analysis of key economic and social indicators across various countries. It includes a diverse range of metrics, allowing for in-depth exploration of global trends related to GDP, education, health, and environmental factors.

Key Features:

• GDP: Gross Domestic Product (in current US dollars), representing the total economic output of a country.
• Sex Ratio: The ratio of males to females in the population, highlighting demographic trends.
• Life Expectancy: Average lifespan for males and females, an essential indicator of healthcare quality.
• Education Enrollment Rates: Data on primary, secondary, and post-secondary education enrollment for males and females, reflecting educational attainment.
• Unemployment Rate: Percentage of the labor force that is unemployed, indicating economic health.
• Homicide Rate: Number of homicides per 100,000 population, providing insight into safety and crime levels.
• Urban Population Growth: Rate of growth in urban populations, illustrating migration trends.
• CO2 Emissions: Carbon dioxide emissions per capita, an important measure of environmental impact.
• Forested Area: Percentage of land covered by forests, indicating biodiversity and environmental health.
• Tourist Numbers: Total number of international visitors, which can reflect a country's tourism potential.

Applications and Uses:

1. Research and Analysis: Ideal for researchers studying the correlation between economic performance and social indicators. This dataset can help identify trends and patterns relevant to global development.

2. Policy Development: Policymakers can utilize this data to inform decisions on education, healthcare, and environmental policies, aiming to improve national outcomes.

3. Machine Learning and Data Science: Data scientists can apply machine learning techniques to predict economic trends, analyze social impacts, or classify countries based on various indicators.

4. Educational Purposes: Suitable for students and educators in fields like economics, sociology, and environmental science for practical data analysis exercises.

5. Visualization Projects: Perfect for creating compelling visualizations that illustrate relationships between different metrics, aiding in public understanding and engagement.

By leveraging this dataset, users can uncover insights into how different factors influence a country's development, making it a valuable resource for diverse applications across various fields.

🚑 Clinical Field Mappings for Healthcare Systems

This synthetic dataset provides a wide variety of alternative names for clinical database fields, mapping them to standardized targets for healthcare data normalization.

Using LLMs, we generated and validated thousands of plausible variations, including misspellings, abbreviations, country-specific nuances, and common real-world typos.

This dataset is perfect for training models that need to standardize, clean, or map heterogeneous healthcare data schemas into unified, normalized formats.

✅ Applications include: - Data cleaning and ETL pipelines for clinical databases - Fine-tuning LLMs for schema matching - Clinical data interoperability projects - Zero-shot field matching research

The dataset is machine-generated and validated with LLM feedback loops to ensure high-quality mappings.

"Facilitate marketing campaigns with the healthcare email list from Infotanks Media that includes doctors, healthcare professionals, NPI numbers, physician specialties, and more. Buy targeted email lists of healthcare professionals and connect with doctors, specialists, and other healthcare professionals to promote your products and services. Hyper personalize campaigns to increase engagement for better chances of conversion. Reach out to our data experts today! Access 1.2 million physician contact database with 150+ specialities including chiropractors, cardiologists, psychiatrists, and radiologists among others. Get ready to integrate healthcare email lists from Infotanks Media to start email marketing campaigns through any CRM and ESP. Contact us right now! Ensure guaranteed lead generation with segmented email marketing strategies for specialists, departments, and more. Make the best use of target marketing to progress and move closer to your business goals with email listing services for healthcare professionals. Infotanks Media provides 100% verified healthcare email lists with the highest email deliverability guarantee of 95%. Get a custom quote today as per your requirements. Enhance your marketing campaigns with healthcare email lists from 170+ countries to build your global outreach. Request your free sample today! Personalize your business communication and interactions to maximize conversion rates with high quality contact data. Grow your business network in your target markets from anywhere in the world with a guaranteed 95% contact accuracy of the healthcare email lists from Infotanks Media. Contact data experts at Infotanks Media from the healthcare industry to get a quick sample for free. Write to us or call today!

Hyper target within and outside your desired markets with GDPR and CAN-SPAM compliant healthcare email lists that get integrated into your CRM and ESPs. Balance out the sales and marketing efforts by aligning goals using email lists from the healthcare industry. Build strong business relationships with potential clients through personalized campaigns. Call Infotanks Media for a free consultation. Explore new geographies and target markets with a focused approach using healthcare email lists. Align your sales teams and marketing teams through personalized email marketing campaigns to ensure they accomplish business goals together. Add value and grow revenue to take your business to the next level of success. Double up your business and revenue growth with email lists of healthcare professionals. Send segmented campaigns to monitor behaviors and understand the purchasing habits of your potential clients. Send follow up nurturing email marketing campaigns to attract your potential clients to become converted customers. Close deals sooner with detailed information of your prospects using the healthcare email list from Infotanks Media. Reach healthcare professionals on their preferred platform of communication with the email list of healthcare professionals. Identify, capture, explore, and grow in your target markets anywhere in the world with a fully verified, validated, and compliant email database of healthcare professionals. Move beyond the traditional approach and automate sales cycles with buying triggers sent through email marketing campaigns. Use the healthcare email list from Infotanks Media to engage with your targeted potential clients and get them to respond. Increase email marketing campaign response rate to convert better! Reach out to Infotanks Media to customize your healthcare email lists. Call today!"

Structural Measures at Hospitals

Investigating Health-Care Quality Across the U.S

By Health [source]

About this dataset

This dataset is an invaluable resource for those who want to understand the impact of structural measures on healthcare. Structural measures are the environment in which hospitals provide their patients with care, from their use of technologies, processes and staff training. This dataset lists hospitals across the nation and each hospital's availability of these structural measures, such as participating in a Cardiac Surgery Registry. With this information one can get an insight into how different aspects of patient care vary according to geographical location and ultimately identify trends in overall health standards regionally. As such we believe that this data could prove invaluable to any researcher working on understanding healthcare disparities or conducting surveys related to patient care assessment

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides a comprehensive list of hospitals and the availability of their structural measures. Structural measures reflect the environment in which hospitals care for patients, such as cleanliness, patient safety, and access to care. To use this dataset, the first step is to identify specific characteristics about the hospital you’re interested in analyzing. Pick out important details like Hospital Name, Address, City, State ZIP Code then search through this dataset. You can compare what type of measure(s) organizations are participating in as well as information on how successful they have been since implementing it/them. After filtering through the data to find what you’re after, take extra steps using measurements from surveys or examining other resources to get a better perspective on how vital these measures are for providing quality care for all patients

Research Ideas

• This dataset can be used to analyze the correlation between the presence of structural measures at a hospital and its impact on patient outcomes.
• The dataset can also be used to map hospitals with certain structural measures across the country for greater access for certain conditions or treatments.
• This data could also be used to study and quantify differences between rural, suburban, and urban hospitals in terms of their access to and implementation of structural measures

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Structural_Measures_-_Hospital.csv | Column name | Description | |:-----------------------|:-----------------------------------------------------------| | Hospital Name | Name of the hospital. (String) | | Address | Street address of the hospital. (String) | | City | City where the hospital is located. (String) | | State | State where the hospital is located. (String) | | ZIP Code | ZIP code of the hospital. (Integer) | | County Name | Name of the county where the hospital is located. (String) | | Phone Number | Phone number of the hospital. (String) | | Measure Name | Name of the structural measure. (String) | | Measure Response | Response to the structural measure. (String) | | Footnote | Footnote associated with the measure response. (String) | | Measure Start Date | Date when the measure was implemented. (Date) | | Measure End Date | Date when the measure was ended. (Date) | | Location | Geographic coordinates of the hospital. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Health.

The first column contains the best hyperparameters for the “Top 13” features, as described in the previous section. The second column contains the best hyperparameters for all features generated.

Analysis of ‘Socio-Economic Country Profiles’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nishanthsalian/socioeconomic-country-profiles on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

There can be multiple motivations for analyzing country specific data, ranging from identifying successful approaches in healthcare policy to identifying business investment opportunities, and many more. Often, all these various goals would have to analyze a substantially overlapping set of parameters. Thus, it would be very good to have a broad set of country specific indicators at one place.

This data-set is an effort in that direction. Of-course there are still plenty more parameters out there. If anyone is interested to integrate more parameters to this dataset, you are more than welcome.

Content

This dataset contains about 95 statistical indicators of the 66 countries. It covers a broad spectrum of areas including

General Information Broader Economic Indicators Social Indicators Environmental & Infrastructure Indicators Military Spending Healthcare Indicators Trade Related Indicators e.t.c.

This data-set for the year 2017 is an amalgamation of data from SRK's Country Statistics - UNData, Numbeo and World Bank.

The entire data-set is contained in one file described below:

soci_econ_country_profiles.csv - The first column contains the country names followed by 95 columns containing the various indicator variables.

Acknowledgements

This is a data-set built on top of SRK's Country Statistics - UNData which was primarily sourced from UNData.

Additional data such as "Cost of living index", "Property price index", "Quality of life index" have been extracted from Numbeo and a number of metrics related to "trade", "healthcare", "military spending", "taxes" etc are extracted from World Bank data source. Given that this is an amalgamation of data from three different sources, only those countries(about 66) which have sufficient data across all the three sources are considered.

Please read the Numbeo terms of use and policieshere Please read the WorldBank terms of use and policies here Please read the UN terms of use and policies here

Photo Credits : Louis Maniquet on Unsplash

--- Original source retains full ownership of the source dataset ---

Explore North America Artificial Intelligence in Healthcare Market, including size, share, growth, trends, and industry analysis, with forecasts extending to 2033.

Background: Rising expenditure for new cancer medicines is accelerating concerns that their costs will become unsustainable for universal healthcare access. Moreover, early market access of new oncology medicines lacking appropriate clinical evaluation generates uncertainty over their cost-effectiveness and increases expenditure for unknown health gain. Patient-level data can complement clinical trials and generate better evidence on the effectiveness, safety and outcomes of these new medicines in routine care. This can support policy decisions including funding. Consequently, there is a need for improving datasets for establishing real-world outcomes of newly launched oncology medicines.Aim: To outline the types of available datasets for collecting patient-level data for oncology among different European countries. Additionally, to highlight concerns regarding the use and availability of such data from a health authority perspective as well as possibilities for cross-national collaboration to improve data collection and inform decision-making.Methods: A mixed methods approach was undertaken through a cross-sectional questionnaire followed-up by a focus group discussion. Participants were selected by purposive sampling to represent stakeholders across different European countries and healthcare settings. Descriptive statistics were used to analyze quantifiable questions, whilst content analysis was employed for open-ended questions.Results: 25 respondents across 18 European countries provided their insights on the types of datasets collecting oncology data, including hospital records, cancer, prescription and medicine registers. The most available is expenditure data whilst data concerning effectiveness, safety and outcomes is less available, and there are concerns with data validity. A major constraint to data collection is the lack of comprehensive registries and limited data on effectiveness, safety and outcomes of new medicines. Data ownership limits data accessibility as well as possibilities for linkage, and data collection is time-consuming, necessitating dedicated staff and better systems to facilitate the process. Cross-national collaboration is challenging but the engagement of multiple stakeholders is a key step to reach common goals through research.Conclusion: This study acts as a starting point for future research on patient-level databases for oncology across Europe. Future recommendations will require continued engagement in research, building on current initiatives and involving multiple stakeholders to establish guidelines and commitments for transparency and data sharing.

Shown are the accuracy, recall and precision for the untuned XGBoost classifier [27] with two subsets of the generated features—all features as well as “Top 13” features described earlier.

Global Total Number of 10% Top-Cited Scientific Publications in Emergency Medical Services by Country, 2023 Discover more data with ReportLinker!

The hyperparameters are selected based on the performance of the “Top 13” features described earlier.

Shown are the accuracy, recall and precision for the tuned XGBoost classifier with the “Top 13” Features described earlier. The hyperparameters are as described in Table 7.

Cancer Mortality & Incidence Rates: (Country LVL)

Investigating Cancer Trends over time

By Data Exercises [source]

About this dataset

This dataset is a comprehensive collection of data from county-level cancer mortality and incidence rates in the United States between 2000-2014. This data provides an unprecedented level of detail into cancer cases, deaths, and trends at a local level. The included columns include County, FIPS, age-adjusted death rate, average death rate per year, recent trend (2) in death rates, recent 5-year trend (2) in death rates and average annual count for each county. This dataset can be used to provide deep insight into the patterns and effects of cancer on communities as well as help inform policy decisions related to mitigating risk factors or increasing preventive measures such as screenings. With this comprehensive set of records from across the United States over 15 years, you will be able to make informed decisions regarding individual patient care or policy development within your own community!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides comprehensive US county-level cancer mortality and incidence rates from 2000 to 2014. It includes the mortality and incidence rate for each county, as well as whether the county met the objective of 45.5 deaths per 100,000 people. It also provides information on recent trends in death rates and average annual counts of cases over the five year period studied.

This dataset can be extremely useful to researchers looking to study trends in cancer death rates across counties. By using this data, researchers will be able to gain valuable insight into how different counties are performing in terms of providing treatment and prevention services for cancer patients and whether preventative measures and healthcare access are having an effect on reducing cancer mortality rates over time. This data can also be used to inform policy makers about counties needing more target prevention efforts or additional resources for providing better healthcare access within at risk communities.

When using this dataset, it is important to pay close attention to any qualitative columns such as “Recent Trend” or “Recent 5-Year Trend (2)” that may provide insights into long term changes that may not be readily apparent when using quantitative variables such as age-adjusted death rate or average deaths per year over shorter periods of time like one year or five years respectively. Additionally, when studying differences between different counties it is important to take note of any standard FIPS code differences that may indicate that data was collected by a different source with a difference methodology than what was used in other areas studied

Research Ideas

• Using this dataset, we can identify patterns in cancer mortality and incidence rates that are statistically significant to create treatment regimens or preventive measures specifically targeting those areas.
• This data can be useful for policymakers to target areas with elevated cancer mortality and incidence rates so they can allocate financial resources to these areas more efficiently.
• This dataset can be used to investigate which factors (such as pollution levels, access to medical care, genetic make up) may have an influence on the cancer mortality and incidence rates in different US counties

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: death .csv | Column name | Description | |:-------------------------------------------|:-------------------------------------------------------------------...

IntroductionDigital mental health care system includes the interventions delivered via digital technologies, such as mobile apps, websites, or virtual reality (VR). A recent upsurge in the digital mental healthcare care services has been observed in the last 5 years. With its core advantage of reaching the unreached, wider coverage, cost and time effectivity, all eyes are on the digital mental health care system. It is definitely a mechanism to cater rising prevalence of mental health concern, stigma towards mental health, accessibility and cost and uplift the psychological wellbeing. Success of the digital mental health care system has been researched world-wide. However, the same is not unaffected by the ethical concerns.MethodsThis study aims to perform a comprehensive bibliometric analysis of scholarly articles on ethical concerns and dilemmas of digital mental health care by utilizing data extracted from the Scopus database from 2000 to 2024 by analysing 123 research articles. Statistical descriptive analysis in combination with performance analysis and co-word analysis was used to understand the research trends, leading countries and country collaborations studying ethical concerns related to digital mental healthcare.Result and discussionThe first publication appeared in 2000 with zero research till the year 2005. In this decade till 2010 we can observe only 4 publications. Consistent publishing started trending upward through 2018, observing the largest increase during pandemic in 2020 and onwards constituting 100 publications. The United States of America is the leading country studying ethical dilemmas in Digital Mental healthcare, with 42 papers followed by United Kingdom with 23 publications. The most influential peace of research with 490 citations is article co-authored by Barak et al. (2009), which is defining internet-supported therapeutic interventions and related concerns. BMJ Open is noted as the leading journal which is publishing issues concerning Digital Mental Healthcare with 18 publications, followed by Frontiers in Psychiatry and JMIR Mental Health. Analyses reflects that the top cited articles on Digital Mental healthcare are specifically directed on bringing out some of the key concerns of data privacy, emergency response, therapist competency and consent which requires appropriate handling Otherwise they may be cause of distress to client and question the trustworthiness of the Digital Mental Health Care system.ConclusionThe concerns brought out through this bibliometric analysis could be important guiding principles for online mental health services. Alongside, mental health professionals operating online must have orientation on the ethical concerns surrounding online mental healthcare.

The number of smokers in Saudi Arabia was forecast to continuously increase between 2024 and 2029 by in total 0.4 million individuals (+8.85 percent). After the fifteenth consecutive increasing year, the number of smokers is estimated to reach 4.88 million individuals and therefore a new peak in 2029. Notably, the number of smokers of was continuously increasing over the past years.Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco, be it on a daily or non-daily basis.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smokers in countries like Qatar and Oman.

Context

COVID-19 virus (coronavirus) has been starting to spread inside of Russia. This is important to investigate and understand the spreading from a very low level. The world-level data is representing Russia virus cases as a single point. But Russia is a very huge and heterogeneous country. For better analyze we have to consider Russia infection cases distributed by region. So this is a dataset of regions distributed COVID-19 virus inside of Russia.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

The dataset is all registered Confirmed/Deaths/Recovered cases of COVID-19 in Russia from January 2020 to present. The table contains daily and cumulative cases. The data is going to be daily updating

Acknowledgements

The data is mainly collected from official government resource. https://rospotrebnadzor.ru/about/info/news/ Some information is grabbed from public resources and local news. https://meduza.io https://coronavirus-monitor.ru https://yandex.ru/company/researches/2020/podomam https://datalens.yandex/7o7is1q6ikh23?tab=q6 https://xn--80aesfpebagmfblc0a.xn--p1ai/information/

World-level datasets https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset https://www.kaggle.com/kimjihoo/coronavirusdataset

Inspiration

How COVID-19 is impacting on Russian society and economy? What is the present situation of COVID-19 spreading in Russia regions? What is the prediction of future COVID-19 spreading in Russia regions?

The number of smokers in the United Arab Emirates was forecast to continuously increase between 2024 and 2029 by in total 0.1 million individuals (+4.13 percent). After the fourteenth consecutive increasing year, the number of smokers is estimated to reach 2.53 million individuals and therefore a new peak in 2029. Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco, be it on a daily or non-daily basis.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smokers in countries like Kuwait and Lebanon.

Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs. Following development and implementation of a formalized review protocol, a cohort of 136 primary studies published between January 2006 and May 2023 were identified from the global literature, with the most widely used techniques and best-performing WBC classification methods subsequently ascertained. Studies derived from 26 countries, with highest numbers from high-income countries including the United States (n = 32) and The Netherlands (n = 26). While WBC classification was originally rooted in conventional ML, there has been a notable shift toward the use of DL, and particularly convolutional neural networks (CNN), with 54.4% of identified studies (n = 74) including the use of CNNs, and particularly in concurrence with larger datasets and bespoke features e.g., parallel data pre-processing, feature selection, and extraction. While some conventional ML models achieved up to 99% accuracy, accuracy was shown to decrease in concurrence with decreasing dataset size. Deep learning models exhibited improved performance for more extensive datasets and exhibited higher levels of accuracy in concurrence with increasingly large datasets. Availability of appropriate datasets remains a primary challenge, potentially resolvable using data augmentation techniques. Moreover, medical training of computer science researchers is recommended to improve current understanding of leucocyte structure and subsequent selection of appropriate classification models. Likewise, it is critical that future health professionals be made aware of the power, efficacy, precision and applicability of computer science, soft computing and artificial intelligence contributions to medicine, and particularly in areas like medical imaging.