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.

📄 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.

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.

In 2022, there were over *** million new cancer cases in China. India, the most populous country in the region and worldwide, had around *** million new cancer cases that year.

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

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...

North America had the highest 12-month cancer prevalence rate in 2022. The 12-month prevalence rate for all cancers in North America as of this time was 595 per 100,000 population. This statistic displays 12-month cancer prevalence rates worldwide in 2022, by region.

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.

This dataset contains cancer statistics for countries members of OECD (The Organization for Economic Co-operation and Development), for OECD key partners and countries in accession negotiations with OECD. The estimated values for the two types of indicators, cancer frequency and cancer incidence, cover the years 1998, 2000, 2002, 2008 and 2012.

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)

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)

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.

Major cancers are associated with lifestyle, and previous studies have found that the non-immigrant populations in the Nordic countries have higher incidence rates of most cancers than the immigrant populations. However, rates are changing worldwide – so these differences may disappear with time. Here we present recent cancer incidence rates among immigrant and non-immigrant men and women in Norway and investigate whether previous differences still exist. We took advantage of a recent change in the Norwegian Cancer Registry regulations that allow for the registry to have information on country of birth. The number of person years for 2014–2018 was aggregated for every combination of sex, five-year age-group and country of birth, by summing up each year’s population in these groups. The number of cancer cases was then counted for the same groups, and age-standardised incidence rates calculated by weighing the age-specific incidence rates by the Nordic and World standard populations. Further, we calculated incidence rate ratios using the non-immigrant population as a reference. Immigrants from Eastern Europe, the Middle East, Africa and Asia had lower incidence of total cancer compared to the non-immigrant population in Norway and immigrants born in the other Nordic or high-income countries. However, some cancers were more common in certain immigrant groups. Asian men and women had threefold the incidence of liver cancer than non-immigrant men and women. Men from the other Nordic countries and from Eastern Europe had higher lung cancer rates than non-immigrant men. National registries should continuously monitor and present cancer incidence stratified on important population subgroups such as country of birth. This can help assess population subgroup specific needs for cancer prevention and treatment, and could eventually help reduce the morbidity and mortality of cancer.

In 2022, Uruguay had the highest age-standardized prevalence rate of all cancer types in Latin America and the Caribbean, with ***** cases per 100,000 population. Barbados and Cuba followed, with cancer prevalence rates of ******* and *******, respectively. That year, Uruguay also had the region's highest mortality death rate.

In 2022, there were around 20 million new cases of cancer worldwide. It is predicted that this number will increase to around 35.3 million incident cases in the year 2050. This statistic shows the predicted number of new cancer cases worldwide from 2022 to 2050.

BackgroundThe nationwide HUN-CANCER EPI study examined cancer incidence and mortality rates in Hungary from 2011 to 2019.MethodsUsing data from the National Health Insurance Fund (NHIF) and Hungarian Central Statistical Office (HCSO), our retrospective study analyzed newly diagnosed malignancies between Jan 1, 2011, and Dec 31, 2019. Age-standardized incidence and mortality rates were calculated for all and for different tumor types using both the 1976 and 2013 European Standard Populations (ESP).FindingsThe number of newly diagnosed cancer cases decreased from 60,554 to 56,675 between 2011–2019. Age-standardized incidence rates were much lower in 2018, than previously estimated (475.5 vs. 580.5/100,000 person-years [PYs] in males and 383.6 vs. 438.5/100,000 PYs in females; ESP 1976). All-site cancer incidence showed a mean annual decrease of 1.9% (95% CI: 2.4%-1.4%) in men and 1.0% (95% CI:1.42%-0.66%) in women, parallel to mortality trends (-1.6% in males and -0.6% in females; ESP 2013). In 2018, the highest age-standardized incidence rates were found for lung (88.3), colorectal (82.2), and prostate cancer (62.3) in men, and breast (104.6), lung (47.7), and colorectal cancer (45.8) in women. The most significant decreases in incidence rates were observed for stomach (4.7%), laryngeal (4.4%), and gallbladder cancers (3.5%), with parallel decreases in mortality rates (3.9%, 2.7% and 3.2%, respectively).InterpretationWe found a lower incidence of newly diagnosed cancer cases for Hungary compared to previous estimates, and decreasing trends in cancer incidence and mortality, in line with global findings and the declining prevalence of smoking.

ObjectiveWe investigated whether there are differences in cancer incidence by geographical area of origin in North-eastern Italy.MethodsWe selected all incident cases recorded in the Veneto Tumour Registry in the period 2015-2019. Subjects were classified, based on the country of birth, in six geographical areas of origin (Italy, Highly Developed Countries-HDC, Eastern Europe, Asia, Africa, South-central America). Age-standardized incidence rates and incidence rate ratio (IRR) were calculated, for all cancer sites and for colorectal, liver, breast and cervical cancer separately.ResultsWe recorded 159,486 all-site cancer cases; 5.2% cases occurred in subjects born outside Italy, the majority from High Migratory Pressure Countries (HMPC) (74.3%). Incidence rates were significantly lower in subjects born in HMPC in both sexes. Immigrants, in particular born in Asia and Africa, showed lower rates of all site cancer incidence. The lowest IRR for colorectal cancer was observed in males from South-Central America (IRR 0.19, 95%CI 0.09-0.44) and in females from Asia (IRR 0.32, 95%CI 0.18-0.70). The IRR of breast cancer appeared significantly lower than Italian natives in all female populations, except for those coming from HDC. Females from Eastern Europe showed a higher IRR for cervical cancer (IRR 2.02, 95%CI 1.57-2.61).ConclusionCancer incidence was found lower in subjects born outside Italy, with differences in incidence patterns depending on geographical area of origin and the cancer type in question. Further studies, focused on the country of birth of the immigrant population, would help to identify specific risk factors influencing cancer incidence.

This dataset contains data about lung cancer Mortality. This database is a comprehensive collection of patient information, specifically focused on individuals diagnosed with cancer. It is designed to facilitate the analysis of various factors that may influence cancer prognosis and treatment outcomes. The database includes a range of demographic, medical, and treatment-related variables, capturing essential details about each patient's condition and history.

Key components of the database include:

Demographic Information: Basic details about the patients such as age, gender, and country of residence. This helps in understanding the distribution of cancer cases across different populations and regions.

Medical History: Information about each patient’s medical background, including family history of cancer, smoking status, Body Mass Index (BMI), cholesterol levels, and the presence of other health conditions such as hypertension, asthma, cirrhosis, and other cancers. This section is crucial for identifying potential risk factors and comorbidities.

Cancer Diagnosis: Detailed data about the cancer diagnosis itself, including the date of diagnosis and the stage of cancer at the time of diagnosis. This helps in tracking the progression and severity of the disease.

Treatment Details: Information regarding the type of treatment each patient received, the end date of the treatment, and the outcome (whether the patient survived or not). This is essential for evaluating the effectiveness of different treatment approaches.

The structure of the database allows for in-depth analysis and research, making it possible to identify patterns, correlations, and potential causal relationships between various factors and cancer outcomes. It is a valuable resource for medical researchers, epidemiologists, and healthcare providers aiming to improve cancer treatment and patient care.

id: A unique identifier for each patient in the dataset. age: The age of the patient at the time of diagnosis. gender: The gender of the patient (e.g., male, female). country: The country or region where the patient resides. diagnosis_date: The date on which the patient was diagnosed with lung cancer. cancer_stage: The stage of lung cancer at the time of diagnosis (e.g., Stage I, Stage II, Stage III, Stage IV). family_history: Indicates whether there is a family history of cancer (e.g., yes, no). smoking_status: The smoking status of the patient (e.g., current smoker, former smoker, never smoked, passive smoker). bmi: The Body Mass Index of the patient at the time of diagnosis. cholesterol_level: The cholesterol level of the patient (value). hypertension: Indicates whether the patient has hypertension (high blood pressure) (e.g., yes, no). asthma: Indicates whether the patient has asthma (e.g., yes, no). cirrhosis: Indicates whether the patient has cirrhosis of the liver (e.g., yes, no). other_cancer: Indicates whether the patient has had any other type of cancer in addition to the primary diagnosis (e.g., yes, no). treatment_type: The type of treatment the patient received (e.g., surgery, chemotherapy, radiation, combined). end_treatment_date: The date on which the patient completed their cancer treatment or died. survived: Indicates whether the patient survived (e.g., yes, no).

This dataset contains artificially generated data with as close a representation of reality as possible. This data is free to use without any licence required.

Good luck Gakusei!