In 2022, the highest cancer rate for men and women among European countries was in Denmark with 728.5 cancer cases per 100,000 population. Ireland and the Netherlands followed, with 641.6 and 641.4 people diagnosed with cancer per 100,000 population, respectively.
Lung cancer Lung cancer is the deadliest type of cancer worldwide, and in Europe, Germany was the country with the highest number of lung cancer deaths in 2022, with 47.7 thousand deaths. However, when looking at the incidence rate of lung cancer, Hungary had the highest for both males and females, with 138.4 and 72.3 cases per 100,000 population, respectively.
Breast cancer Breast cancer is the most common type of cancer among women with an incidence rate of 83.3 cases per 100,000 population in Europe in 2022. Cyprus was the country with the highest incidence of breast cancer, followed by Belgium and France. The mortality rate due to breast cancer was 34.8 deaths per 100,000 population across Europe, and Cyprus was again the country with the highest figure.

This dataset contains information about cancer rate for 50 countries in the world. The data was obtained by doing web scraping from Wikipedia using BeautifulSoup in Python.

Wikipedia link: https://en.wikipedia.org/wiki/List_of_countries_by_cancer_rate

Image source: https://unsplash.com/photos/L7en7Lb-Ovc?utm_source=unsplash&utm_medium=referral&utm_content=creditShareLink

In 2022, Croatia reported 355.7 deaths from cancer per 100,000 population, the highest cancer mortality rate in Europe. Hungary followed with 332.8 cancer deaths per 100,000, and then Italy with 325.6 cancer deaths per 100,000 population. This statistic displays the mortality rate of cancer in Europe in 2022, by country (per 100,000 population).

This dataset provides detailed information on global cancer incidence rates and numbers for both males and females in the year 2022. It includes data on various types of cancer, both including and excluding non-melanoma skin cancer (NMSC). The dataset is organized into two CSV files:

Global cancer incidence in males and females (2022).csv: Contains detailed data for individual countries, including cancer incidence rates and numbers for both males and females, categorized by including and excluding NMSC. Overall global cancer incidence (2022).csv: Provides an aggregated view of global cancer incidence, summarizing key statistics across different regions and demographics.

Dataset Description This dataset contains information on cancer deaths by country, type, and year. It includes data on 18 different types of cancer, including liver cancer, kidney cancer, larynx cancer, breast cancer, thyroid cancer, stomach cancer, bladder cancer, uterine cancer, ovarian cancer, cervical cancer, prostate cancer, pancreatic cancer, esophageal cancer, testicular cancer, nasopharynx cancer, other pharynx cancer, colon and rectum cancer, non-melanoma skin cancer, lip and oral cavity cancer, brain and nervous system cancer, tracheal, bronchus, and lung cancer, gallbladder and biliary tract cancer, malignant skin melanoma, leukemia, Hodgkin lymphoma, multiple myeloma, and other cancers.

Data Fields The dataset includes the following data fields:

• Country: The country where the cancer death occurred.
• Code: The country code for the country where the cancer death occurred.
• Year: The year in which the cancer death occurred.
• Liver cancer: The number of cancer deaths from liver cancer in the country in the year.
• Kidney cancer: The number of cancer deaths from kidney cancer in the country in the year.
• Larynx cancer: The number of cancer deaths from larynx cancer in the country in the year.
• Breast cancer: The number of cancer deaths from breast cancer in the country in the year.
• Thyroid cancer: The number of cancer deaths from thyroid cancer in the country in the year.
• Stomach cancer: The number of cancer deaths from stomach cancer in the country in the year.
• Bladder cancer: The number of cancer deaths from bladder cancer in the country in the year.
• Uterine cancer: The number of cancer deaths from uterine cancer in the country in the year.
• Ovarian cancer: The number of cancer deaths from ovarian cancer in the country in the year.
• Cervical cancer: The number of cancer deaths from cervical cancer in the country in the year.
• Prostate cancer: The number of cancer deaths from prostate cancer in the country in the year.
• Pancreatic cancer: The number of cancer deaths from pancreatic cancer in the country in the year.
• Esophageal cancer: The number of cancer deaths from esophageal cancer in the country in the year.
• Testicular cancer: The number of cancer deaths from testicular cancer in the country in the year.
• Nasopharynx cancer: The number of cancer deaths from nasopharynx cancer in the country in the year.
• Other pharynx cancer: The number of cancer deaths from other pharynx cancer in the country in the year.
• Colon and rectum cancer: The number of cancer deaths from colon and rectum cancer in the country in the year.
• Non-melanoma skin cancer: The number of cancer deaths from non-melanoma skin cancer in the country in the year.
• Lip and oral cavity cancer: The number of cancer deaths from lip and oral cavity cancer in the country in the year.
• Brain and nervous system cancer: The number of cancer deaths from brain and nervous system cancer in the country in the year.
• Tracheal, bronchus, and lung cancer: The number of cancer deaths from tracheal, bronchus, and lung cancer in the country in the year.
• Gallbladder and biliary tract cancer: The number of cancer deaths from gallbladder and biliary tract cancer in the country in the year.
• Malignant skin melanoma: The number of cancer deaths from malignant skin melanoma in the country in the year.
• Leukemia: The number of cancer deaths from leukemia in the country in the year.
• Hodgkin lymphoma: The number of cancer deaths from Hodgkin lymphoma in the country in the year.
• Multiple myeloma: The number of cancer deaths from multiple myeloma in the country in the year.
• Other cancers: The number of cancer deaths from other cancers in the country in the year.

Data Source The data in this dataset was collected from the World Health Organization (WHO). The WHO collects data on cancer deaths from countries around the world.

Usage This dataset can be used to study cancer deaths by country, type, and year. It can also be used to compare cancer death rates between different countries or over time.

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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!

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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 | |:-------------------------------------------|:-------------------------------------------------------------------...

In 2022, the mortality rate of breast cancer in women in Europe was **** per 100,000 women. Cyprus had the highest mortality rate at **** per 100,000, followed by Slovakia with **** per 100,000 women. Conversely, Spain had the lowest mortality rate at **** per 100,000. This statistic depicts the mortality rate of breast cancer in Europe in 2022 in women population, by country.

To investigate the global incidence of prostate cancer with special attention to the changing age structures. Data regarding the cancer incidence and population statistics were retrieved from the International Agency for Research on Cancer in World Health Organization. Eight developing and developed jurisdictions in Asia and the Western countries were selected for global comparison. Time series were constructed based on the cancer incidence rates from 1988 to 2007. The incidence rate of the population aged ≥ 65 was adjusted by the increasing proportion of elderly population, and was defined as the “aging-adjusted incidence rate”. Cancer incidence and population were then projected to 2030. The aging-adjusted incidence rates of prostate cancer in Asia (Hong Kong, Japan and China) and the developing Western countries (Costa Rica and Croatia) had increased progressively with time. In the developed Western countries (the United States, the United Kingdom and Sweden), we observed initial increases in the aging-adjusted incidence rates of prostate cancer, which then gradually plateaued and even decreased with time. Projections showed that the aging-adjusted incidence rates of prostate cancer in Asia and the developing Western countries were expected to increase in much larger extents than the developed Western countries.

Users can access data about cancer statistics, specifically incidence and mortality worldwide for the 27 major types of cancer. Background Cancer Mondial is maintained by the Section of Cancer Information (CIN) of International Agency for Research on Cancer by the World Health Organization. Users can access CIN databases including GLOBOCAN, CI5(Cancer Incidence in Five Continents), WHO, ACCIS(Automated Childhood Cancer Information System), ECO (European Cancer Observatory), NORDCAN and Survcan. User functionality Users can access a variety of databases. CIN Databases: GLOBOCAN provides acces s to the most recent estimates (for 2008) of the incidence of 27 major cancers and mortality from 27 major cancers worldwide. CI5 (Cancer Incidence in Five Continents) provides access to detailed information on the incidence of cancer recorded by cancer registries (regional or national) worldwide. WHO presents long time series of selected cancer mortality recorded in selected countries of the world. Collaborative projects: ACCIS (Automated Childhood Cancer Information System) provides access to data on cancer incidence and survival of children collected by European cancer registries. ECO (European Cancer Observatory) provides access to the estimates (for 2008) of the incidence of, and mortality f rom 25 major cancers in the countries of the European Union (EU-27). NORDCAN presents up-to-date long time series of cancer incidence, mortality, prevalence and survival from 40 cancers recorded by the Nordic countries. SurvCan presents cancer survival data from cancer registries in low and middle income regions of the world. Data Notes Data is available in different formats depending on which type of data is accessed. Some data is available in table, PDF, and html formats. Detailed information about the data is available.

Editor’s Choice

• Global Breast Cancer Market size is expected to be worth around USD 49.2 Bn by 2032 from USD 19.8 Bn in 2022, growing at a CAGR of 9.8% during the forecast period from 2022 to 2032.
• Breast cancer is the most common cancer among women worldwide. In 2020, there were about 2.3 million new cases of breast cancer diagnosed globally.
• Breast cancer is the leading cause of cancer-related deaths in women. In 2020, it was responsible for approximately 685,000 deaths worldwide.
• The survival rate of breast cancer has improved over the years. In the United States, the overall five-year survival rate of breast cancer is around 90%.
• The American Cancer Society recommends annual mammograms starting at age 40 for women at average risk.
• Although rare, breast cancer also occurs in men. Less than 1% of breast cancer cases are diagnosed in males.

(Source: WHO, American Cancer Society)

This dataset provides a detailed view of global cancer trends across the 50 most populated countries. With 160,000 records, it encompasses a wide range of variables including cancer types, risk factors, healthcare expenditure, and environmental factors. The data is designed to assist researchers, healthcare policymakers, and data scientists in identifying patterns, predicting future trends, and crafting effective cancer control strategies.

Exploring County-Level Correlations in Cancer Rates and Trends

A Multivariate Ordinary Least Squares Regression Model

By Noah Rippner [source]

About this dataset

This dataset offers a unique opportunity to examine the pattern and trends of county-level cancer rates in the United States at the individual county level. Using data from cancer.gov and the US Census American Community Survey, this dataset allows us to gain insight into how age-adjusted death rate, average deaths per year, and recent trends vary between counties – along with other key metrics like average annual counts, met objectives of 45.5?, recent trends (2) in death rates, etc., captured within our deep multi-dimensional dataset. We are able to build linear regression models based on our data to determine correlations between variables that can help us better understand cancers prevalence levels across different counties over time - making it easier to target health initiatives and resources accurately when necessary or desired

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How to use the dataset

This kaggle dataset provides county-level datasets from the US Census American Community Survey and cancer.gov for exploring correlations between county-level cancer rates, trends, and mortality statistics. This dataset contains records from all U.S counties concerning the age-adjusted death rate, average deaths per year, recent trend (2) in death rates, average annual count of cases detected within 5 years, and whether or not an objective of 45.5 (1) was met in the county associated with each row in the table.

To use this dataset to its fullest potential you need to understand how to perform simple descriptive analytics which includes calculating summary statistics such as mean, median or other numerical values; summarizing categorical variables using frequency tables; creating data visualizations such as charts and histograms; applying linear regression or other machine learning techniques such as support vector machines (SVMs), random forests or neural networks etc.; differentiating between supervised vs unsupervised learning techniques etc.; reviewing diagnostics tests to evaluate your models; interpreting your findings; hypothesizing possible reasons and patterns discovered during exploration made through data visualizations ; Communicating and conveying results found via effective presentation slides/documents etc.. Having this understanding will enable you apply different methods of analysis on this data set accurately ad effectively.

Once these concepts are understood you are ready start exploring this data set by first importing it into your visualization software either tableau public/ desktop version/Qlikview / SAS Analytical suite/Python notebooks for building predictive models by loading specified packages based on usage like Scikit Learn if Python is used among others depending on what tool is used . Secondly a brief description of the entire table's column structure has been provided above . Statistical operations can be carried out with simple queries after proper knowledge of basic SQL commands is attained just like queries using sub sets can also be performed with good command over selecting columns while specifying conditions applicable along with sorting operations being done based on specific attributes as required leading up towards writing python codes needed when parsing specific portion of data desired grouping / aggregating different categories before performing any kind of predictions / models can also activated create post joining few tables possible , when ever necessary once again varying across tools being used Thereby diving deep into analyzing available features determined randomly thus creating correlation matrices figures showing distribution relationships using correlation & covariance matrixes , thus making evaluations deducing informative facts since revealing trends identified through corresponding scatter plots from a given metric gathered from appropriate fields!

Research Ideas

• Building a predictive cancer incidence model based on county-level demographic data to identify high-risk areas and target public health interventions.
• Analyzing correlations between age-adjusted death rate, average annual count, and recent trends in order to develop more effective policy initiatives for cancer prevention and healthcare access.
• Utilizing the dataset to construct a machine learning algorithm that can predict county-level mortality rates based on socio-economic factors such as poverty levels and educational attainment rates

Acknowledgements

If you use this dataset i...

Context

Cancer Stats for OECD Countries Visualization

Content

Cancer Stats for OECD Countries

Acknowledgements

OECD Stats Website - https://stats.oecd.org/

Inspiration

Kaggle

📄 Dataset Description: This dataset contains global cancer patient data reported from 2015 to 2024, designed to simulate the key factors influencing cancer diagnosis, treatment, and survival. It includes a variety of features that are commonly studied in the medical field, such as age, gender, cancer type, environmental factors, and lifestyle behaviors. The dataset is perfect for:

Exploratory Data Analysis (EDA)

Multiple Linear Regression and other modeling tasks

Feature Selection and Correlation Analysis

Predictive Modeling for cancer severity, treatment cost, and survival prediction

Data Visualization and creating insightful graphs

Key Features: Age: Patient's age (20-90 years)

Gender: Male, Female, or Other

Country/Region: Country or region of the patient

Cancer Type: Various types of cancer (e.g., Breast, Lung, Colon)

Cancer Stage: Stage 0 to Stage IV

Risk Factors: Includes genetic risk, air pollution, alcohol use, smoking, obesity, etc.

Treatment Cost: Estimated cost of cancer treatment (in USD)

Survival Years: Years survived since diagnosis

Severity Score: A composite score representing cancer severity

This dataset provides a broad view of global cancer trends, making it an ideal resource for those learning data science, machine learning, and statistical analysis in healthcare.

In 2022, Australia had the fourth-highest total number of skin cancer cases worldwide and the highest age-standardized rate, with roughly 37 cases of skin cancer per 100,000 population. The graph illustrates the rate of skin cancer in the countries with the highest skin cancer rates worldwide in 2022.

In 2018, Hungary reported ***** new cases of colorectal cancer per 100,000 population, the highest number of colorectal cancer cases in Europe. In the same year, Belgium reported to have the highest number of new breast cancer cases (females only) with ***** cases per 100,000 population, while Sweden reported to have the highest number of new prostate cancer cases with ***** cases per 100,000 population. This statistic shows the number of new cancer cases per 100,000 population for selected types of cancers in European countries in 2018.

Global Breast Cancer Screening (Programme Data) by Country, 2023 Discover more data with ReportLinker!

Context

Data containing life expectancy at birth by sex and geography in Europe. Source is https://ec.europa.eu/eurostat/data/database (official European Data Source). Data is downloaded from the source, documented and uploaded to Kaggle.

Content

Original data

The original data is provided in TSV (tab delimited) format. The dataset gives the number of deaths by cancer (icd10 classification ac C) by sex and geography in Europe (yearly data). The time unit used are years. Geography is at country level (in Europe) or aggregated on 2 indicators for Europe (EU27_2020 & EU28). Sex is F or M. ICD-10 indicator is C, meaning neoplasm (Cancer).

Transformed data

For your convenience, we transformed the data in csv format; see procedure in Starter Kernel: Deaths by Cancer in Europe.

How to

The data has the temporal information given as columns (per year). In order to further use this data, it would be more easy to pivot first these columns to get instead date/value pairs. This pivot operation, using melt from pandas is done in the starter kernel: * Starter Kernel: Deaths by Cancer in Europe; we convert the year to an integer. Just run this Kernel to put the data in csv format, with yearly data pivoted.

Acknowledgements

All merit for data collection, curation, and initial publishing goes to Eurostat.

Inspiration

You can use this data for various demographic, public health, social aspects, combining with alternative data from Kaggle and other sources.

This dataset contains real-world information about colorectal cancer cases from different countries. It includes patient demographics, lifestyle risks, medical history, cancer stage, treatment types, survival chances, and healthcare costs. The dataset follows global trends in colorectal cancer incidence, mortality, and prevention.

Use this dataset to build models for cancer prediction, survival analysis, healthcare cost estimation, and disease risk factors.

Dataset Structure Each row represents an individual case, and the columns include:

Patient_ID (Unique identifier) Country (Based on incidence distribution) Age (Following colorectal cancer age trends) Gender (M/F, considering men have 30-40% higher risk) Cancer_Stage (Localized, Regional, Metastatic) Tumor_Size_mm (Randomized within medical limits) Family_History (Yes/No) Smoking_History (Yes/No) Alcohol_Consumption (Yes/No) Obesity_BMI (Normal/Overweight/Obese) Diet_Risk (Low/Moderate/High) Physical_Activity (Low/Moderate/High) Diabetes (Yes/No) Inflammatory_Bowel_Disease (Yes/No) Genetic_Mutation (Yes/No) Screening_History (Regular/Irregular/Never) Early_Detection (Yes/No) Treatment_Type (Surgery/Chemotherapy/Radiotherapy/Combination) Survival_5_years (Yes/No) Mortality (Yes/No) Healthcare_Costs (Country-dependent, $25K-$100K+) Incidence_Rate_per_100K (Country-level prevalence) Mortality_Rate_per_100K (Country-level mortality) Urban_or_Rural (Urban/Rural) Economic_Classification (Developed/Developing) Healthcare_Access (Low/Moderate/High) Insurance_Status (Insured/Uninsured) Survival_Prediction (Yes/No, based on factors)

According to Cognitive Market Research, the global Cancer Diagnosis market size was USD 109614.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 6.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 43845.80 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 32884.35 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 25211.34 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.5% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 5480.73 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 2192.29 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
The consumables category is the fastest growing segment of the Cancer Diagnosis industry

Market Dynamics of Cancer Diagnosis Market

Key Drivers for Cancer Diagnosis Market

Increasing Rate of Cancer Diagnostics to Boost Market Growth

The rising global incidence of cancer, which affects millions of people a year, is a primary driver of the need for diagnostic testing. Numerous factors contribute to this tendency, such as the aging population, which increases the risk of developing some cancers in older adults. Changes in lifestyle, including poor eating habits, inactivity, and increased use of alcohol and tobacco, have also contributed to an increase in cancer incidence. Environmental factors, such as exposure to chemicals and hazardous compounds, exacerbate the problem and increase the risk of developing cancer. Therefore, as early detection and diagnosis are becoming more and more important to patients and healthcare professionals, effective cancer diagnostics are essential. The market for cancer diagnostics is expanding as a result of the increased emphasis on prompt and precise cancer detection, which highlights the value of novel diagnostic procedures. For Instance, in 2023, the Pan American Health Organization (PAHO) projects that there will be 20 million new cases and 10 million deaths, and by 2040, nearly 30 million cases will be reported annually.

Innovations in Diagnostic Technologies to Drive Market Growth

The market for cancer diagnostics is expanding as a result of advancements in diagnostic technologies that have greatly improved the precision and effectiveness of cancer detection. For example, non-invasive cancer biomarker identification in physiological fluids is made possible by liquid biopsies, which offer vital insights into tumor dynamics and therapy response. In a similar vein, molecular diagnostics has transformed the detection of particular genetic abnormalities and changes linked to different types of cancer, allowing for more individualized treatment strategies. High-resolution images of tumors are provided by advanced imaging methods like MRI and PET scans, which help with accurate staging and localization. Better patient outcomes result from these technical developments because they increase overall diagnosis accuracy and enable early intervention. The ongoing development of these cutting-edge diagnostic instruments is propelling market expansion and revolutionizing cancer treatment.

Restraint Factor for the Cancer Diagnosis Market

The High Price of Cutting-Edge Diagnostic Technology Will Limit Market Growth

The market for cancer diagnostics is severely hampered by the high price of sophisticated diagnostic tools. Advanced diagnostic instruments, such as molecular tests and imaging technologies, are frequently expensive, which limits healthcare facilities' access to them, especially in settings with limited resources. These institutions' capacity to provide thorough cancer screening and diagnostic services is restricted by this financial barrier, which eventually affects patient outcomes. These financial difficulties are further exacerbated by the costs associated with the development, research, and regulatory approval of new diagnostic instruments. Companies have to spend a lot of money to comply with...