46 datasets found
  1. a

    CDC Cancer Deaths (Lung and Colon)

    • data.amerigeoss.org
    Updated Aug 13, 2022
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    United States (2022). CDC Cancer Deaths (Lung and Colon) [Dataset]. https://data.amerigeoss.org/dataset/cdc-cancer-deaths-lung-and-colon
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    Dataset updated
    Aug 13, 2022
    Dataset provided by
    United States
    Description

    This map service portrays the number of deaths per 100,000 people per square mile from lung and colon cancer. It displays the distribution of lung and colon cancer across the United States. Pop-ups show attributes such as state name, county name, number of colon or lung cancer deaths, and square miles per area.Lung Cancer: Death due to malignant neoplasm of the trachea, bronchus and lung.Colon Cancer: Death due to malignant neoplasm of the colon, rectum and anus.This data was sourced from: Community Health Status Indicators_Other Health Datapalooza focused content that may interest you: Health Datapalooza Health Datapalooza

  2. l

    Lung Cancer Mortality

    • data.lacounty.gov
    • geohub.lacity.org
    Updated Dec 20, 2023
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    County of Los Angeles (2023). Lung Cancer Mortality [Dataset]. https://data.lacounty.gov/datasets/lung-cancer-mortality/about
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    Dataset updated
    Dec 20, 2023
    Dataset authored and provided by
    County of Los Angeles
    Area covered
    Description

    Death rate has been age-adjusted by the 2000 U.S. standard population. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Lung cancer is a leading cause of cancer-related death in the US. People who smoke have the greatest risk of lung cancer, though lung cancer can also occur in people who have never smoked. Most cases are due to long-term tobacco smoking or exposure to secondhand tobacco smoke. Cities and communities can take an active role in curbing tobacco use and reducing lung cancer by adopting policies to regulate tobacco retail; reducing exposure to secondhand smoke in outdoor public spaces, such as parks, restaurants, or in multi-unit housing; and improving access to tobacco cessation programs and other preventive services.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

  3. d

    Compendium – Mortality from lung cancer

    • digital.nhs.uk
    Updated Jul 21, 2021
    + more versions
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    Compendium – Mortality from lung cancer [Dataset]. https://digital.nhs.uk/data-and-information/publications/statistical/compendium-mortality/current/mortality-from-lung-cancer
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    Dataset updated
    Jul 21, 2021
    License

    https://digital.nhs.uk/about-nhs-digital/terms-and-conditionshttps://digital.nhs.uk/about-nhs-digital/terms-and-conditions

    Description

    To reduce deaths from lung cancer. For information on the definitions of what these indicators include, please see the relevant specification. From 2016 onwards, mortality counts within the Compendium Mortality Indicator set are based on a bespoke extract taken from the Primary Care Mortality Database (PCMD) maintained by NHS Digital. PCMD is updated monthly using a file of death records from ONS and is continually subject to amendment. It is already well established that late registrations have a small impact on counts. This bespoke extract may be taken at a different time to that of the mortality data published by ONS and as such this may cause some small differences between ONS and NHS Digital mortality figures for a given year.

  4. Number and rates of new cases of primary cancer, by cancer type, age group...

    • www150.statcan.gc.ca
    • datasets.ai
    • +2more
    Updated May 19, 2021
    + more versions
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    Government of Canada, Statistics Canada (2021). Number and rates of new cases of primary cancer, by cancer type, age group and sex [Dataset]. http://doi.org/10.25318/1310011101-eng
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    Dataset updated
    May 19, 2021
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Number and rate of new cancer cases diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

  5. c

    National Lung Screening Trial

    • cancerimagingarchive.net
    • dev.cancerimagingarchive.net
    dicom, docx, n/a +2
    Updated Sep 24, 2021
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    The Cancer Imaging Archive (2021). National Lung Screening Trial [Dataset]. http://doi.org/10.7937/TCIA.HMQ8-J677
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    docx, svs, dicom, n/a, sas, zip, and docAvailable download formats
    Dataset updated
    Sep 24, 2021
    Dataset authored and provided by
    The Cancer Imaging Archive
    License

    https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/

    Time period covered
    Sep 24, 2021
    Dataset funded by
    National Cancer Institutehttp://www.cancer.gov/
    Description

    https://www.cancerimagingarchive.net/wp-content/uploads/nctn-logo-300x108.png" alt="" width="300" height="108" />

    Demographic Summary of Available Imaging

    CharacteristicValue (N = 26254)
    Age (years)Mean ± SD: 61.4± 5
    Median (IQR): 60 (57-65)
    Range: 43-75
    SexMale: 15512 (59%)
    Female: 10742 (41%)
    Race

    White: 23969 (91.3%)
    Black: 1135 (4.3%)
    Asian: 547 (2.1%)
    American Indian/Alaska Native: 88 (0.3%)
    Native Hawaiian/Other Pacific Islander: 87 (0.3%)
    Unknown: 428 (1.6%)

    Ethnicity

    Not Available

    Background: The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer.

    Methods: From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009. This dataset includes the low-dose CT scans from 26,254 of these subjects, as well as digitized histopathology images from 451 subjects.

    Results: The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02).

    Conclusions: Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385).

    Data Availability: A summary of the National Lung Screening Trial and its available datasets are provided on the Cancer Data Access System (CDAS). CDAS is maintained by Information Management System (IMS), contracted by the National Cancer Institute (NCI) as keepers and statistical analyzers of the NLST trial data. The full clinical data set from NLST is available through CDAS. Users of TCIA can download without restriction a publicly distributable subset of that clinical data, along with the CT and Histopathology images collected during the trial. (These previously were restricted.)

  6. Lung Cancer

    • kaggle.com
    Updated Jul 15, 2022
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    Ms. Nancy Al Aswad (2022). Lung Cancer [Dataset]. https://www.kaggle.com/datasets/nancyalaswad90/lung-cancer/discussion?sort=undefined
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 15, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Ms. Nancy Al Aswad
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    What is Lung Cancer Dataset?

    The effectiveness of the cancer prediction system helps people to know their cancer risk at a low cost and it also helps the people to take the appropriate decision based on their cancer risk status. The data is collected from the website online lung cancer prediction system.

    .

    https://user-images.githubusercontent.com/36210723/182395183-ef7519e3-9c18-47ac-b7a6-a00e234f3949.png" alt="2022-08-02_170741">

    .

    Acknowledgments

    When we use this dataset in our research, we credit the authors as :

    • License : CC BY 4.0.

    • Hong, Z.Q. and Yang, J.Y. "Optimal Discriminant Plane for a Small Number of Samples and Design Method of Classifier on the Plane", Pattern Recognition, Vol. 24, No. 4, pp. 317-324, 1991 and it is published t to reuse in google research dataset

    The main idea for uploading this dataset is to practice data analysis with my students, as I am working in college and want my student to train our studying ideas in a big dataset, It may be not up to date and I mention the collecting years, but it is a good resource of data to practice

  7. Lung cancer Bangladesh

    • kaggle.com
    Updated Mar 15, 2025
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    NISHAT VASKER (2025). Lung cancer Bangladesh [Dataset]. http://doi.org/10.34740/kaggle/dsv/11035259
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 15, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    NISHAT VASKER
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    Bangladesh
    Description

    About Dataset 📌 Overview This dataset has been carefully synthesized to support research in lung cancer survival prediction, enabling the development of models that estimate:

    Whether a patient is likely to survive at least one year post-diagnosis (Binary Classification). The probability of survival based on clinical and lifestyle factors (Regression Analysis). The dataset is designed for machine learning and deep learning applications in medical AI, oncology research, and predictive healthcare.

    📜 Dataset Generation Process The dataset was generated using a combination of real-world epidemiological insights, medical literature, and statistical modeling. The feature distributions and relationships have been carefully modeled to reflect real-world clinical scenarios, ensuring biomedical validity.

    📖 Medical References & Sources The dataset structure is based on well-established lung cancer risk factors and survival indicators documented in leading medical research and clinical guidelines:

    World Health Organization (WHO) Reports on lung cancer epidemiology. National Cancer Institute (NCI) & American Cancer Society (ACS) guidelines on lung cancer risk factors and treatment outcomes. The IASLC Lung Cancer Staging Project (8th Edition): Standard reference for lung cancer staging. Harrison’s Principles of Internal Medicine (20th Edition): Provides an in-depth review of lung cancer diagnosis and treatment. Lung Cancer: Principles and Practice (2022, Oxford University Press): Clinical insights into lung cancer detection, treatment, and survival factors. 🔬 Features of the Dataset Each record in the dataset represents an individual’s clinical condition, lifestyle risk factors, and survival outcome. The dataset includes the following features:

    1️⃣ Patient Demographics Age → A key risk factor for lung cancer progression and survival. Gender → Male and female lung cancer survival rates can differ. Residence → Urban vs. Rural (impact of environmental factors). 2️⃣ Risk Factors & Lifestyle Indicators These factors have been linked to lung cancer risk in epidemiological studies:

    Smoking Status → (Current Smoker, Former Smoker, Never Smoked). Air Pollution Exposure → (Low, Moderate, High). Biomass Fuel Use → (Yes/No) – Associated with household air pollution. Factory Exposure → (Yes/No) – Industrial exposure increases lung cancer risk. Family History → (Yes/No) – Genetic predisposition to lung cancer. Diet Habit → (Vegetarian, Non-Vegetarian, Mixed) – Nutritional impact on cancer progression. 3️⃣ Symptoms (Primary Predictors) These are key clinical indicators associated with lung cancer detection and severity:

    Hemoptysis (Coughing Blood) Chest Pain Fatigue & Weakness Chronic Cough Unexplained Weight Loss 4️⃣ Tumor Characteristics & Clinical Features Tumor Size (mm) → The size of the detected tumor. Histology Type → (Adenocarcinoma, Squamous Cell Carcinoma, Small Cell Carcinoma). Cancer Stage → (Stage I to Stage IV). 5️⃣ Treatment & Healthcare Facility Treatment Received → (Surgery, Chemotherapy, Radiation, Targeted Therapy). Hospital Type → (Private, Government, Medical College). 6️⃣ Target Variables (Predicted Outcomes) Survival (Binary) → 1 (Yes) if the patient survives at least 1 year, 0 (No) otherwise. Survival Probability (%) (Can be derived) → Estimated probability of survival within one year. ⚡ Why This Dataset is Valuable? ✅ Balanced Data Distribution Designed to ensure a representative distribution of lung cancer survival cases. Prevents model bias and improves generalization in predictive models. ✅ Medically-Inspired Feature Engineering Features are derived from real-world lung cancer risk factors, validated through medical literature. Incorporates both lifestyle and clinical indicators to enhance predictive accuracy.(no real person data is used,just have made an biomedical environment) ✅ Diverse Risk Factors Considered Smoking, air pollution, and genetic history as primary lung cancer contributors. Symptom severity and tumor histology influence survival rates. ✅ Scalability & ML Suitability Ideal for classification and regression tasks in machine learning. Can be used with deep learning (TensorFlow, PyTorch), ML models (XGBoost, Random Forest, SVM), and explainable AI techniques like SHAP and LIME. 📂 Dataset Usage & Applications This dataset is highly useful for multiple healthcare AI applications, including:

    🩺 Predictive Analytics → Early detection of high-risk lung cancer patients. 🤖 Healthcare Chatbots → AI-powered risk assessment tools.

  8. A

    ‘ lung cancer’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 28, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘ lung cancer’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-lung-cancer-8f99/8c3676e0/?iid=000-288&v=presentation
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    Dataset updated
    Jan 28, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘ lung cancer’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/h13380436001/h-lung-cancer on 28 January 2022.

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

    Gender: M(male), F(female) Age: Age of the patient Smoking: YES=2 , NO=1. Yellow fingers: YES=2 , NO=1. Anxiety: YES=2 , NO=1. Peer_pressure: YES=2 , NO=1. Chronic Disease: YES=2 , NO=1. Fatigue: YES=2 , NO=1. Allergy: YES=2 , NO=1. Wheezing: YES=2 , NO=1. Alcohol: YES=2 , NO=1. Coughing: YES=2 , NO=1. Shortness of Breath: YES=2 , NO=1. Swallowing Difficulty: YES=2 , NO=1. Chest pain: YES=2 , NO=1. Lung Cancer: YES , NO.

    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.

    Acknowledgements

    We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

    Inspiration

    Your data will be in front of the world's largest data science community. What questions do you want to see answered?

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

  9. g

    Five-year survival from breast, lung and colorectal cancer (NHSOF 1.4.iv) |...

    • gimi9.com
    Updated Jul 8, 2014
    + more versions
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    (2014). Five-year survival from breast, lung and colorectal cancer (NHSOF 1.4.iv) | gimi9.com [Dataset]. https://gimi9.com/dataset/uk_five-year-survival-from-breast-lung-and-colorectal-cancer-nhsof-1-4-iv/
    Explore at:
    Dataset updated
    Jul 8, 2014
    Description

    A measure of the number of adults diagnosed with breast, lung or colorectal cancer in a year who are still alive five years after diagnosis. ONS still publish survival percentages for individual types of cancers. These can be found at: http://www.ons.gov.uk/ons/rel/cancer-unit/cancer-survival/cancer-survival-in-england--patients-diagnosed-2007-2011-and-followed-up-to-2012/index.html A time series for five-year survival figures for breast, lung and colorectal cancer individually (previous NHS Outcomes Framework indicators 1.4.ii, 1.4.iv and 1.4.vi) is still published and can be found under the link 'Indicator data - previous methodology (.xls)' below. Purpose This indicator attempts to capture the success of the NHS in preventing people from dying once they have been diagnosed with breast, lung or colorectal cancer. Current version updated: May-14 Next version due: To be confirmed

  10. One-year survival from breast, lung and colorectal cancer (NHSOF 1.4.iii)

    • data.europa.eu
    • cloud.csiss.gmu.edu
    csv, excel xls
    Updated Oct 30, 2021
    + more versions
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    NHS Digital (2021). One-year survival from breast, lung and colorectal cancer (NHSOF 1.4.iii) [Dataset]. https://data.europa.eu/data/datasets/one-year-survival-from-breast-lung-and-colorectal-cancer-nhsof-1-4-iii?locale=hu
    Explore at:
    csv, excel xlsAvailable download formats
    Dataset updated
    Oct 30, 2021
    Dataset authored and provided by
    NHS Digitalhttps://digital.nhs.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    A measure of the number of adults diagnosed with breast, lung or colorectal cancer in a year who are still alive one year after diagnosis.

    ONS still publish survival percentages for individual types of cancers. These can be found at: http://www.ons.gov.uk/ons/rel/cancer-unit/cancer-survival/cancer-survival-in-england--patients-diagnosed-2007-2011-and-followed-up-to-2012/index.html

    A time series for one-year survival figures for breast, lung and colorectal cancer individually (previous NHS Outcomes Framework indicators 1.4.i, 1.4.iii and 1.4.v) is still published and can be found under the link 'Indicator data - previous methodology (.xls)' below.

    Purpose

    This indicator attempts to capture the success of the NHS in preventing people from dying once they have been diagnosed with breast, lung or colorectal cancer.

    Current version updated: Feb-14

    Next version due: To be confirmed

  11. Mortality and potential years of life lost, by selected causes of death and...

    • www150.statcan.gc.ca
    • data.urbandatacentre.ca
    • +2more
    Updated Mar 16, 2016
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    Government of Canada, Statistics Canada (2016). Mortality and potential years of life lost, by selected causes of death and sex, three-year average, census metropolitan areas occasional (number) [Dataset]. http://doi.org/10.25318/1310074101-eng
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    Dataset updated
    Mar 16, 2016
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    This table contains 33048 series, with data for years 2000/2002 - 2010/2012 (not all combinations necessarily have data for all years), and was last released on 2016-03-16. This table contains data described by the following dimensions (Not all combinations are available): Geography (36 items: Total, census metropolitan areas; St. John's, Newfoundland and Labrador; Halifax, Nova Scotia;Moncton, New Brunswick; ...), Sex (3 items: Both sexes; Males; Females), Indicators (2 items: Mortality; Potential years of life lost), Selected causes of death (ICD-10) (17 items: Total, all causes of death; All malignant neoplasms (cancers); Colorectal cancer; Lung cancer; ...), Characteristics (9 items: Number; Low 95% confidence interval, number; High 95% confidence interval, number; Rate; ...).

  12. Cancer survival in England - adults diagnosed

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Aug 12, 2019
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    Office for National Statistics (2019). Cancer survival in England - adults diagnosed [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/datasets/cancersurvivalratescancersurvivalinenglandadultsdiagnosed
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    xlsxAvailable download formats
    Dataset updated
    Aug 12, 2019
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    One-year and five-year net survival for adults (15-99) in England diagnosed with one of 29 common cancers, by age and sex.

  13. r

    AIHW - Cancer Incidence and Mortality Across Regions (CIMAR) - Persons...

    • researchdata.edu.au
    null
    Updated Jun 28, 2023
    + more versions
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    AIHW - Cancer Incidence and Mortality Across Regions (CIMAR) - Persons Mortality (GCCSA) 2009-2013 [Dataset]. https://researchdata.edu.au/aihw-cancer-incidence-2009-2013/2738757
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    nullAvailable download formats
    Dataset updated
    Jun 28, 2023
    Dataset provided by
    Australian Urban Research Infrastructure Network (AURIN)
    Authors
    Government of the Commonwealth of Australia - Australian Institute of Health and Welfare
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Area covered
    Description

    This dataset presents the footprint of cancer mortality statistics in Australia for all cancers combined and the 6 top cancer groupings (colorectal, leukaemia, lung, lymphoma, melanoma of the skin and pancreas) and their respective ICD-10 codes. The data spans the years 2009-2013 and is aggregated to Greater Capital City Statistical Areas (GCCSA) from the 2011 Australian Statistical Geography Standard (ASGS).

    Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD).

    For further information about this dataset, please visit:

    Please note:

    • AURIN has spatially enabled the original data.

    • Due to changes in geographic classifications over time, long-term trends are not available.

    • Values assigned to "n.p." in the original data have been removed from the data.

    • The Australian and jurisdictional totals include people who could not be assigned a GCCSA. The number of people who could not be assigned a GCCSA is less than 1% of the total.

    • The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory).

    • Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the NMD.

    • Year refers to year of occurrence of death for years up to and including 2012, and year of registration of death for 2013. Deaths registered in 2011 and earlier are based on the final version of cause of death data; deaths registered in 2012 and 2013 are based on revised and preliminary versions, respectively and are subject to further revision by the ABS.

    • Cause of death information are based on underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10).

    • Colorectal deaths presented are underestimates. For further information, refer to "Complexities in the measurement of bowel cancer in Australia" in Causes of Death, Australia (ABS cat. no. 3303.0).

  14. f

    Data from: Dataset description.

    • plos.figshare.com
    xls
    Updated Aug 27, 2024
    + more versions
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    Dataset description. [Dataset]. https://plos.figshare.com/articles/dataset/Dataset_description_/26034390
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    xlsAvailable download formats
    Dataset updated
    Aug 27, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Refat Khan Pathan; Israt Jahan Shorna; Md. Sayem Hossain; Mayeen Uddin Khandaker; Huda I. Almohammed; Zuhal Y. Hamd
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Among many types of cancers, to date, lung cancer remains one of the deadliest cancers around the world. Many researchers, scientists, doctors, and people from other fields continuously contribute to this subject regarding early prediction and diagnosis. One of the significant problems in prediction is the black-box nature of machine learning models. Though the detection rate is comparatively satisfactory, people have yet to learn how a model came to that decision, causing trust issues among patients and healthcare workers. This work uses multiple machine learning models on a numerical dataset of lung cancer-relevant parameters and compares performance and accuracy. After comparison, each model has been explained using different methods. The main contribution of this research is to give logical explanations of why the model reached a particular decision to achieve trust. This research has also been compared with a previous study that worked with a similar dataset and took expert opinions regarding their proposed model. We also showed that our research achieved better results than their proposed model and specialist opinion using hyperparameter tuning, having an improved accuracy of almost 100% in all four models.

  15. H

    National Occupational Respiratory Mortality System (NORMS)

    • data.niaid.nih.gov
    Updated Mar 31, 2011
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    (2011). National Occupational Respiratory Mortality System (NORMS) [Dataset]. http://doi.org/10.7910/DVN/ZATO3A
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    Dataset updated
    Mar 31, 2011
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Users can search this database pertaining to respiratory conditions such as asthma, pneumonia, bronchitis, and tuberculosis. BackgroundThe National Occupational Respiratory Mortality System (NORMS) is developed and maintained by National Institute of Occupational Safety and Health (NIOSH) of the Centers for Disease Control and Prevention (CDC). This surveillance system includes respiratory conditions such as: asthma, pneumonia, bronchitis, tuberculosis, lung cancer, and silicosis, among others. User FunctionalityUsers can generate national- or occupation-specific queries. Users can gener ate tables, charts and maps containing the summary statistics such as number of deaths, crude death rates, age-adjusted death rates, and years of potential life lost (YPLL ). Users can also download the dataset and/or data queries into Microsoft Excel. Data NotesThis website provides data history regarding revisions to the dataset. Data from additional sources (i.e., population estimates, comparative standard population, and life-table values) are also available. National mortality data is derived from the National Center for Health Statistics (NCHS) multiple cause of death records. These data are updated annually since 1968, unless otherwise indicated. Data are available on national, state, and county levels. The most recent d ata available is from 2007.

  16. a

    AIHW - Cancer Incidence and Mortality Across Regions (CIMAR) - Males...

    • data.aurin.org.au
    Updated Mar 6, 2025
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    (2025). AIHW - Cancer Incidence and Mortality Across Regions (CIMAR) - Males Incidence (SA4) 2006-2010 - Dataset - AURIN [Dataset]. https://data.aurin.org.au/dataset/au-govt-aihw-aihw-cimar-incidence-males-sa4-2006-10-sa4
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    Dataset updated
    Mar 6, 2025
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    This dataset presents the footprint of male cancer incidence statistics in Australia for all cancers combined and the 11 top cancer groupings (bladder, colorectal, head and neck, kidney, leukaemia, lung, lymphoma, melanoma of the skin, pancreas, prostate and stomach) and their respective ICD-10 codes. The data spans the years 2006-2010 and is aggregated to Statistical Area Level 4 (SA4) from the 2011 Australian Statistical Geography Standard (ASGS). Incidence data refer to the number of new cases of cancer diagnosed in a given time period. It does not refer to the number of people newly diagnosed (because one person can be diagnosed with more than one cancer in a year). Cancer incidence data come from the Australian Institute of Health and Welfare (AIHW) 2012 Australian Cancer Database (ACD).

  17. a

    AIHW - Cancer Incidence and Mortality Across Regions (CIMAR) - Persons...

    • data.aurin.org.au
    Updated Mar 6, 2025
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    (2025). AIHW - Cancer Incidence and Mortality Across Regions (CIMAR) - Persons Incidence (SA4) 2006-2010 - Dataset - AURIN [Dataset]. https://data.aurin.org.au/dataset/au-govt-aihw-aihw-cimar-incidence-persons-sa4-2006-10-sa4
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    Dataset updated
    Mar 6, 2025
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    This dataset presents the footprint of cancer incidence statistics in Australia for all cancers combined and the 6 top cancer groupings (colorectal, leukaemia, lung, lymphoma, melanoma of the skin and pancreas) and their respective ICD-10 codes. The data spans the years 2006-2010 and is aggregated to Statistical Area Level 4 (SA4) from the 2011 Australian Statistical Geography Standard (ASGS). Incidence data refer to the number of new cases of cancer diagnosed in a given time period. It does not refer to the number of people newly diagnosed (because one person can be diagnosed with more than one cancer in a year). Cancer incidence data come from the Australian Institute of Health and Welfare (AIHW) 2012 Australian Cancer Database (ACD).

  18. a

    AIHW - Cancer Incidence and Mortality Across Regions (CIMAR) - Females...

    • data.aurin.org.au
    Updated Mar 6, 2025
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    (2025). AIHW - Cancer Incidence and Mortality Across Regions (CIMAR) - Females Incidence (SA4) 2006-2010 - Dataset - AURIN [Dataset]. https://data.aurin.org.au/dataset/au-govt-aihw-aihw-cimar-incidence-females-sa4-2006-10-sa4
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    Dataset updated
    Mar 6, 2025
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    This dataset presents the footprint of female cancer incidence statistics in Australia for all cancers combined and the 11 top cancer groupings (breast, cervical, colorectal, leukaemia, lung, lymphoma, melanoma of the skin, ovary, pancreas, thyroid and uterus) and their respective ICD-10 codes. The data spans the years 2006-2010 and is aggregated to Statistical Area Level 4 (SA4) from the 2011 Australian Statistical Geography Standard (ASGS). Incidence data refer to the number of new cases of cancer diagnosed in a given time period. It does not refer to the number of people newly diagnosed (because one person can be diagnosed with more than one cancer in a year). Cancer incidence data come from the Australian Institute of Health and Welfare (AIHW) 2012 Australian Cancer Database (ACD).

  19. Adult Smoking Prevalence - Datasets - Lincolnshire Open Data

    • lincolnshire.ckan.io
    Updated May 23, 2017
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    lincolnshire.ckan.io (2017). Adult Smoking Prevalence - Datasets - Lincolnshire Open Data [Dataset]. https://lincolnshire.ckan.io/dataset/adult-smoking-prevalence
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    Dataset updated
    May 23, 2017
    Dataset provided by
    CKANhttps://ckan.org/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    This data shows the percentage of adults (age 18 and over) who are current smokers. Smoking is the single biggest cause of preventable death and illnesses, and big inequalities exist between and within communities. Smoking is a major risk factor for many diseases, such as lung cancer, chronic obstructive pulmonary disease (COPD, bronchitis and emphysema) and heart disease. It is also associated with cancers in other organs. Smoking is a modifiable lifestyle risk factor. Preventing people from starting smoking is important in reducing the health harms and inequalities. This data is based on the Office for National Statistics (ONS) Annual Population Survey (APS). The percentage of adults is not age-standardised. In this dataset particularly at district level there may be inherent statistical uncertainty in some data values. Thus as with many other datasets, this data should be used together with other data and resources to obtain a fuller picture. Data source: Office for Health Improvement and Disparities (OHID) Public Health Outcomes Framework (PHOF) indicator 92443 (Number 15). This data is updated annually.

  20. f

    DataSheet_1_Development and validation of machine learning models to predict...

    • frontiersin.figshare.com
    txt
    Updated Jun 21, 2023
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    Long Jin; Qifan Zhao; Shenbo Fu; Fei Cao; Bin Hou; Jia Ma (2023). DataSheet_1_Development and validation of machine learning models to predict survival of patients with resected stage-III NSCLC.csv [Dataset]. http://doi.org/10.3389/fonc.2023.1092478.s001
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    txtAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    Frontiers
    Authors
    Long Jin; Qifan Zhao; Shenbo Fu; Fei Cao; Bin Hou; Jia Ma
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ObjectiveTo compare the performance of three machine learning algorithms with the tumor, node, and metastasis (TNM) staging system in survival prediction and validate the individual adjuvant treatment recommendations plan based on the optimal model.MethodsIn this study, we trained three machine learning madel and validated 3 machine learning survival models-deep learning neural network, random forest and cox proportional hazard model- using the data of patients with stage-al3 NSCLC patients who received resection surgery from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database from 2012 to 2017,the performance of survival predication from all machine learning models were assessed using a concordance index (c-index) and the averaged c-index is utilized for cross-validation. The optimal model was externally validated in an independent cohort from Shaanxi Provincial People’s Hospital. Then we compare the performance of the optimal model and TNM staging system. Finally, we developed a Cloud-based recommendation system for adjuvant therapy to visualize survival curve of each treatment plan and deployed on the internet.ResultsA total of 4617 patients were included in this study. The deep learning network performed more stably and accurately in predicting stage-iii NSCLC resected patients survival than the random survival forest and Cox proportional hazard model on the internal test dataset (C-index=0.834 vs. 0.678 vs. 0.640) and better than TNM staging system (C-index=0.820 vs. 0.650) in the external validation. The individual patient who follow the reference from recommendation system had superior survival compared to those who did not. The predicted 5-year-survival curve for each adjuvant treatment plan could be accessed in the recommender system via the browser.ConclusionDeep learning model has several advantages over linear model and random forest model in prognostic predication and treatment recommendations. This novel analytical approach may provide accurate predication on individual survival and treatment recommendations for resected Stage-iii NSCLC patients.

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United States (2022). CDC Cancer Deaths (Lung and Colon) [Dataset]. https://data.amerigeoss.org/dataset/cdc-cancer-deaths-lung-and-colon

CDC Cancer Deaths (Lung and Colon)

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Dataset updated
Aug 13, 2022
Dataset provided by
United States
Description

This map service portrays the number of deaths per 100,000 people per square mile from lung and colon cancer. It displays the distribution of lung and colon cancer across the United States. Pop-ups show attributes such as state name, county name, number of colon or lung cancer deaths, and square miles per area.Lung Cancer: Death due to malignant neoplasm of the trachea, bronchus and lung.Colon Cancer: Death due to malignant neoplasm of the colon, rectum and anus.This data was sourced from: Community Health Status Indicators_Other Health Datapalooza focused content that may interest you: Health Datapalooza Health Datapalooza

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