• Skin diseases encompass a broad spectrum of conditions affecting the largest organ of the human body, ranging from common dermatological issues to more severe and potentially life-threatening disorders. Skin cancers, a subset of skin diseases, specifically involve the abnormal and uncontrolled growth of skin cells, often triggered by exposure to ultraviolet (UV) radiation, genetic factors, or environmental influences. Skin cancers, including melanoma, basal cell carcinoma, and squamous cell carcinoma, pose a significant health concern globally due to their prevalence and potential for metastasis. On the other hand, non-cancerous skin diseases, such as eczema, psoriasis, and acne, impact millions, affecting quality of life and sometimes leading to complications if left untreated. Research in this field is vital for understanding the complexities of skin diseases and cancers, developing effective detection methods, advancing treatment options, and ultimately improving outcomes for individuals affected by these conditions.

• Early detection, accurate diagnosis, and targeted interventions are key elements in the ongoing efforts to mitigate the impact of skin diseases and cancers on public health.

• In recent times, computer vision has shown great promise in conducting the classification and identification tasks of this kind.

• Fifty seven distinct kinds of skin diseases and skin cancer are shown in this large dataset, which can be used to develop machine vision-based techniques.

• In this dataset, there are 978 (primary source 90, secondary source 888) original images of skin diseases and skin cancer. Then, in order to increase the number of data points, shifting, flipping, zooming, shearing, brightness enhancement, and rotation techniques are used to create a total of 630 augmented images from these original images (primary source).

Skin cancers represent the most common human tumors with a worldwide increasing incidence. They can be divided into melanoma and non-melanoma skin cancers (NMSCs). NMSCs include mainly squamous cell (SCC) and basal cell carcinoma (BCC) with the latest representing the 80% of the diagnosed NMSCs. The pathogenesis of NMSCs is clearly multifactorial. A growing body of literature underlies a crucial correlation between skin cancer, chronic inflammation and immunodeficiency. Intensity and duration of immunodeficiency plays an important role. In immunocompromised patients the incidence of more malignant forms or the development of multiple tumors seems to be higher than among immunocompetent patients. With regards to people living with HIV (PLWH), since the advent of combined antiretroviral therapy (cART), the incidence of non-AIDS-defining cancers (NADCs), such as NMSCs, have been increasing and now these neoplasms represent a leading cause of illness in this particular population. PLWH with NMSCs tend to be younger, to have a higher risk of local recurrence and to have an overall poorer outcome. NMSCs show an indolent clinical course if diagnosed and treated in an early stage. BCC rarely metastasizes, while SCC presents a 4% annual incidence of metastasis. Nevertheless, metastatic forms lead to poor patient outcome. NMSCs are often treated with full thickness treatments (surgical excision, Mohs micro-graphic surgery and radiotherapy) or superficial ablative techniques (such as cryotherapy, electrodesiccation and curettage). Advances in genetic landscape understanding of NMSCs have favored the establishment of novel therapeutic strategies. Concerning the therapeutic evaluation of PLWH, it’s mandatory to evaluate the risk of interactions between cART and other treatments, particularly antiblastic chemotherapy, targeted therapy and immunotherapy. Development of further treatment options for NMSCs in PLWH seems needed. We reviewed the literature after searching for clinical trials, case series, clinical cases and available databases in Embase and Pubmed. We review the incidence of NMSCs among PLWH, focusing our attention on any differences in clinicopathological features of BCC and SCC between PLWH and HIV negative persons, as well as on any differences in efficacy and safety of treatments and response to immunomodulators and finally on any differences in rates of metastatic disease and outcomes.

Data License

The data is signed off as non-disclosive and is released under an Open Government Licence. link

This work uses data that has been provided by patients and collected by the NHS as part of their care and support. The data are collated, maintained and quality assured by the National Disease Registration Service, which is part of NHS England.

Recommended Reading

A previous project of mine on Kaggle; The benefits of early diagnosis are manifold. As documented in the project, diagnosing cancer in its nascent stages significantly bolsters survival rates, elevates the experience and quality of care received by patients, enhances the overall quality of life, and importantly, drives down both the costs and intricacies associated with cancer treatments. Such benefits underscore the profound importance of prompt diagnosis and also cast a light on the tangible repercussions of delays in such processes.

Cancer is a serious business ! Can technology be leveraged to help an early diagnosis ? link

Introduction

Cancer remains one of the most critical health challenges worldwide, impacting millions and posing substantial burdens on healthcare systems. Early diagnosis is a key factor in improving cancer outcomes, as it allows for timely intervention, often leading to better survival rates and improved quality of life for patients. This project uses data collected by the NHS, managed by the National Disease Registration Service under the Open Government License, to explore patterns in cancer incidence, diagnostic pathways, and survival rates across various cancer types. Through data visualisation and statistical analysis, this work seeks to deepen our understanding of the factors influencing early diagnosis, the effectiveness of different diagnostic routes, and the progression of survival rates over time

Data Visualisation: GDO_data_wide.csv

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The above chart displays the average percentages of different cancer presentation methods across various cancer sites.

Key observations about the chart: - Emergency Presentation (Red): This is a common presentation method for many cancers, especially pancreatic and brain cancers. This likely reflects the difficulty in detecting these cancers early. - GP Referral (Orange): A significant proportion of cancers are diagnosed via GP referral, highlighting the importance of primary care in cancer detection. This is particularly noticeable for skin cancer. - Two-Week Wait (Green): This is most prominent for suspected testicular, prostate, head and neck cancers. - Screening (Blue): Plays a crucial role in detecting specific cancers, notably breast and cervical cancers, where established screening programs exist. However, the impact is small. - Other Outpatient (Purple): This is prominent for eye cancer and varies across other cancer types, likely encompassing a range of planned diagnostic procedures and follow-up appointments.

By combining the information from the chart, we can gain a clearer understanding of how different cancers are typically diagnosed. This information can be valuable for raising awareness, promoting early detection, and improving cancer care.

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The cleaned summary tabl...

Population based cancer incidence rates were abstracted from National Cancer Institute, State Cancer Profiles for all available counties in the United States for which data were available. This is a national county-level database of cancer data that are collected by state public health surveillance systems. All-site cancer is defined as any type of cancer that is captured in the state registry data, though non-melanoma skin cancer is not included. All-site age-adjusted cancer incidence rates were abstracted separately for males and females. County-level annual age-adjusted all-site cancer incidence rates for years 2006–2010 were available for 2687 of 3142 (85.5%) counties in the U.S. Counties for which there are fewer than 16 reported cases in a specific area-sex-race category are suppressed to ensure confidentiality and stability of rate estimates; this accounted for 14 counties in our study. Two states, Kansas and Virginia, do not provide data because of state legislation and regulations which prohibit the release of county level data to outside entities. Data from Michigan does not include cases diagnosed in other states because data exchange agreements prohibit the release of data to third parties. Finally, state data is not available for three states, Minnesota, Ohio, and Washington. The age-adjusted average annual incidence rate for all counties was 453.7 per 100,000 persons. We selected 2006–2010 as it is subsequent in time to the EQI exposure data which was constructed to represent the years 2000–2005. We also gathered data for the three leading causes of cancer for males (lung, prostate, and colorectal) and females (lung, breast, and colorectal). The EQI was used as an exposure metric as an indicator of cumulative environmental exposures at the county-level representing the period 2000 to 2005. A complete description of the datasets used in the EQI are provided in Lobdell et al. and methods used for index construction are described by Messer et al. The EQI was developed for the period 2000– 2005 because it was the time period for which the most recent data were available when index construction was initiated. The EQI includes variables representing each of the environmental domains. The air domain includes 87 variables representing criteria and hazardous air pollutants. The water domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., L. Messer, K. Rappazzo , C. Gray, S. Grabich , and D. Lobdell. County-level environmental quality and associations with cancer incidence#. Cancer. John Wiley & Sons Incorporated, New York, NY, USA, 123(15): 2901-2908, (2017).

This database encompasses several files related to cancer data. The first file is an Excel spreadsheet, containing information on newly diagnosed cancer cases from 2014 to 2017. It provides demographic details and specific characteristics of 482,229 cancer patients. We categorized this data according to the International Agency for Research on Cancer (IARC) reporting rules, and cancers with greater incidence rates were identified. To create a geodatabase, individual data was integrated at the county level and combined with population data. Files 2 and 3 contain gender-specific spatial data for the top cancer types and non-melanoma skin cancer. Each file includes county identifications, the number of cancer cases for each cancer type per year, and gender-specific population information. Lastly, there is a user's guide file to help navigate through the data files.

Importance: Skin cancer, in particular squamous cell carcinoma, is the most frequent malignancy among solid organ transplant recipients with a higher incidence compared to the general population. Objective: To determine the skin cancer incidence in organ transplant recipients in Switzerland and to assess the impact of immunosuppressants and other risk factors. Design: Prospective cohort study of solid organ transplant recipients in Switzerland enrolled in the Swiss Transplant Cohort Study from 2008 to 2013. Participants: 2,192 solid organ transplant recipients. Materials and Methods: Occurrence of first and subsequent squamous cell carcinoma, basal cell carcinoma, melanoma and other skin cancers after transplantation extracted from the Swiss Transplant Cohort Study database and validated by medical record review. Incidence rates were calculated for skin cancer overall and subgroups. The effect of risk factors on the occurrence of first skin cancer and recurrent skin cancer was calculated by the Cox proportional hazard model. Results: In 2,192 organ transplant recipients, 136 (6.2%) developed 335 cases of skin cancer during a median follow-up of 32.4 months, with squamous cell carcinoma as the most frequent one. 79.4% of skin cancer patients were male. Risk factors for first and recurrent skin cancer were age at transplantation, male sex, skin cancer before transplantation and previous transplantation. For a first skin cancer, the number of immunosuppressive drugs was a risk factor as well. Conclusions and Relevance: Skin cancer following solid organ transplantation in Switzerland is greatly increased with risk factors: age at transplantation, male sex, skin cancer before transplantation, previous transplantation and number of immunosuppressive drugs.

Context

There's a story behind every dataset and here's your opportunity to share yours.

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. Welcome to our regular closed dataset based weekend hackathon. In this weekend hackathon, we are challenging all the machinehackers to predict the melanoma tumor size based on various attributes. Melanomas present in many different shapes, sizes, and colors. That’s why it’s tricky to provide a comprehensive set of warning signs. Melanoma, also known as malignant melanoma, is a type of skin cancer that develops from the pigment-producing cells known as melanocytes. The primary cause of melanoma is ultraviolet light (UV) exposure in those with low levels of the skin pigment melanin. The UV light may be from the sun or other sources, such as tanning devices.

Melanoma is the most dangerous type of skin cancer. Globally, in 2012, it newly occurred in 232,000 people. In 2015, there were 3.1 million people with active disease, which resulted in 59,800 deaths. Australia and New Zealand have the highest rates of melanoma in the world. There are also high rates in Northern Europe and North America, while it is less common in Asia, Africa, and Latin America. In the United States melanoma occurs about 1.6 times more often in men than women.

Data Description:

Train.csv - 9146 rows x 9 columns
Test.csv - 36584 rows x 8 columns
Sample Submission - Acceptable submission format 

Attributes Description:

mass_npea: the mass of the area understudy for melanoma tumor
size_npear: the size of the area understudy for melanoma tumor
malign_ratio: ration of normal to malign surface understudy
damage_size: unrecoverable area of skin damaged by the tumor
exposed_area: total area exposed to the tumor
std_dev_malign: standard deviation of malign skin measurements
err_malign: error in malign skin measurements
malign_penalty: penalty applied due to measurement error in the lab
damage_ratio: the ratio of damage to total spread on the skin
tumor_size: size of melanoma_tumor

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?

IntroductionThe incidence of melanoma has been increasing in the last decades. A retrospective Hungarian epidemiological study provided real-world data on incidence and mortality rates. There have been changing trends in incidence in Hungary in the last decade and mortality decreased, shifting mortality-to-incidence rate ratios (MIR). MIR is an indicator of cancer management quality.ObjectivesOur aim is to show the changes of melanoma MIR in Hungary between 2011 and 2018 and to compare the real-world evidence-based results of our Hungarian nationwide retrospective study with other European countries.MethodsMIR is calculated from the age-specific standardized incidence and mortality rates from our study. Annual MIR values are presented for the total population and for both sexes between 2011 and 2018, along with 95% confidence intervals. Comparison with European countries are shown for 2012 and 2018 based on the GLOBOCAN database and Eurostat health care expenditure per capita data.ResultsMIR decreased by 0.035 during the study years. The decrease was same in both sexes (0.031). Male had higher MIRs in all study years. In both 2012 and 2018, Hungarian MIR in both sexes was lower than the European Union average (males: 0.192 vs. 0.212 and 0.148 vs. 0.174 respectively, women: 0.107 vs. 0.129 and 0.083 vs. 0.107 respectively).DiscussionHungarian mortality-to-incidence ratio is the lowest in Central and Eastern Europe and is close to the level of Western and Northern European countries. The results are driven by the high number of new diagnosed melanoma cases.

This dataset contains summary data visualizations and clinical data from a broad sampling of 442 samples of skin cutaneous melanomas from 488 patients. The data was gathered as part of the PanCancer Atlas initiative, which aims to answer big, overarching questions about cancer by examining the full set of tumors characterized in the robust TCGA dataset. The clinical data includes mutation count, information about mutated genes, patient demographics, disease status, tumor typing, and chromosomal gain or loss. The data set also includes copy-number segment data downloadable as .seg files and viewable via the Integrative Genomics Viewer.

Shown are the absolute numbers of the six subcategories “red/high” and “yellow/medium” with their allocation to the three risk groups by each algorithm (a and b). 3c shows the histopathological results of all excised lesions. 3d and 3e display crosstabulations of the respective algorithm with the clinical category.

Overall comparison of EFFNet with existing models on HAM10000 dataset.

New Zealand and Australia have the world’s highest rates of melanoma, the most serious type of skin cancer. Melanoma is mainly caused by exposure to ultraviolet (UV) light, usually from the sun. New Zealand has naturally high UV levels, especially during summer.
The risk of developing melanoma is affected by factors such as skin colour and type, family history, and the amount of sun exposure. Melanoma can affect people at any age, but the chance of developing a melanoma increases with age. We report on age-standardised rates of melanoma to account for the increasing proportion of older people in our population. Our data on melanoma registrations come from the New Zealand Cancer Registry and the Ministry of Health's Mortality Collection. The passing of the Cancer Registry Act 1993 and Cancer Registry Regulations 1994 led to significant improvements in data quality and coverage (Ministry of Health, 2013). A sharp increase in registrations after 1993 is likely to have been related to these legislative and regulatory changes; for this reason we have only analysed data from 1996. 2014–15 data are provisional and subject to change. More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

New Zealand and Australia have the world’s highest rates of melanoma, the most serious type of skin cancer. Melanoma is mainly caused by exposure to ultraviolet (UV) light, usually from the sun. New Zealand has naturally high UV levels, especially during summer.
The risk of developing melanoma is affected by factors such as skin colour and type, family history, and the amount of sun exposure. Melanoma can affect people at any age, but the chance of developing a melanoma increases with age. We report on age-standardised rates of melanoma to account for the increasing proportion of older people in our population. Our data on melanoma registrations come from the New Zealand Cancer Registry and the Ministry of Health's Mortality Collection. The passing of the Cancer Registry Act 1993 and Cancer Registry Regulations 1994 led to significant improvements in data quality and coverage (Ministry of Health, 2013). A sharp increase in registrations after 1993 is likely to have been related to these legislative and regulatory changes; for this reason we have only analysed data from 1996. Trend direction was assessed using the Theil-Sen estimator and the Two One-Sided Test (TOST) for equivalence at the 95% confidence level. More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Context

Welcome to our regular closed dataset based weekend hackathon. In this weekend hackathon, we are challenging all the machinehackers to predict the melanoma tumor size based on various attributes. Melanomas present in many different shapes, sizes, and colors. That’s why it’s tricky to provide a comprehensive set of warning signs. Melanoma, also known as malignant melanoma, is a type of skin cancer that develops from the pigment-producing cells known as melanocytes. The primary cause of melanoma is ultraviolet light (UV) exposure in those with low levels of the skin pigment melanin. The UV light may be from the sun or other sources, such as tanning devices.

Melanoma is the most dangerous type of skin cancer. Globally, in 2012, it newly occurred in 232,000 people. In 2015, there were 3.1 million people with active disease, which resulted in 59,800 deaths. Australia and New Zealand have the highest rates of melanoma in the world. There are also high rates in Northern Europe and North America, while it is less common in Asia, Africa, and Latin America. In the United States melanoma occurs about 1.6 times more often in men than women.

Content

Train.csv - 9146 rows x 9 columns Test.csv - 36584 rows x 8 columns Sample Submission - Acceptable submission format

Attributes

Acknowledgements

Machine Hack: Melanoma Tumor Size Prediction

BackgroundSkin cutaneous melanoma (SKCM) is the deadliest skin cancer and has the most rapidly increasing incidences among all cancer types. Previous research elucidated that melanoma can only be successfully treated with surgical abscission in the early stage. Therefore, reliable and specific biomarkers are crucial to melanoma diagnosis since it often looks like nevi in the clinical manifestations. Moreover, identifying key genes contributing to melanoma progression is also highly regarded as a potential strategy for melanoma therapy. In this respect, translation initiator eIF6 has been proved as a pro-tumor factor in several cancers. However, the role of eIF6 in the skin cutaneous melanoma progression and its potential as a prognostic marker is still unexplored.MethodsThe immunochemical analysis of clinical specimens were served to assess eIF6 expression levels. Gene Expression Profiling Interactive Analysis (GEPIA) database consultations allowed us to find the survival rates of the eIF6-overexpressed patients. eIF6 cellular effects were evaluated in an eIF6-overexpressed A375 cell line constructed with a lentivirus. The analysis of down-stream effectors or pathways was conducted using C-Bioportal and STRING databases.ResultsOur results revealed that eIF6 was highly over-expressed in melanomas compared to normal skin specimens, and thus the abnormally high level of eIF6 can be a diagnostic marker for melanoma. The in silica analysis indicated that patients with eIF6 over-expression had lower survival rates than that low-expression in SKCM. Meanwhile, similar results also could be found in the other four types of cancers. In vitro, over-expression of eIF6 increased the proliferation and migration of melanoma cells. Correspondingly, pan-cancer clustering analysis indicated the expression level of intermediate filament proteins was correlated with that of eIF6 expression. In our study, all over-expressed keratin proteins, in accordance with over-expressed eIF6, had a negative correlation with melanoma prognosis. Moreover, the decreased methylation level of keratin genes suggested a new potential regulation mode of eIF6.ConclusionsThe up-regulated eIF6 could be a potential diagnostic and prognostic biomarker of melanoma. This study also provides insights into the potential role of eIF6 in pan-cancer epigenetic regulation.

This dataset provides insights into one-year survival rates from all cancers, serving as a key indicator of early cancer outcomes. It measures the proportion of individuals diagnosed with an invasive cancer who survive for at least one year following their diagnosis. The dataset includes all invasive tumours classified under ICD-10 codes C00 to C97, excluding non-melanoma skin cancer (C44). It supports analysis across different population groups and geographies, including ethnicity, deprivation levels, and the Birmingham and Solihull (BSol) area.

Rationale

Improving one-year survival rates is a critical goal in cancer care, as it reflects the effectiveness of early diagnosis and initial treatment. This indicator helps monitor progress in reducing early mortality from cancer and supports targeted interventions to improve outcomes.

Numerator

The numerator includes individuals who were diagnosed with a specific type of cancer and died from the same type of cancer within one year of diagnosis. Only invasive cancers are included, as defined by ICD-10 codes C00 to C97, excluding non-melanoma skin cancer (C44). Data is sourced from the National Cancer Registration and Analysis Service (NCRAS).

Denominator

The denominator comprises all individuals diagnosed with an invasive cancer (ICD-10 codes C00 to C97, excluding C44) within a five-year period. This data is also sourced from the National Cancer Registration and Analysis Service (NCRAS).

Caveats

This dataset uses a simplified methodology that differs from the national calculation of one-year cancer survival. As a result, the figures presented here may not align with nationally published statistics. However, this approach enables the provision of survival data disaggregated by ethnicity, deprivation, and local geographies such as BSol, which is not always possible with national data.

External references

For more information, visit the National Cancer Registration and Analysis Service (NCRAS).

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

This dataset presents the footprint of cancer incidence statistics in Australia for all cancers combined and the 5 top cancer groupings (breast - female only, colorectal, lung, melanoma of the skin …Show full descriptionThis dataset presents the footprint of cancer incidence statistics in Australia for all cancers combined and the 5 top cancer groupings (breast - female only, colorectal, lung, melanoma of the skin and prostate) and their respective ICD-10 codes. The data spans the years 2006-2010 and is aggregated to Statistical Area Level 3 (SA3) 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). For further information about this dataset, please visit: Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books. Australian Cancer Database 2012 Data Quality Statement. 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 SA3. The number of people who could not be assigned a SA3 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). The ACD records all primary cancers except for basal and squamous cell carcinomas of the skin (BCCs and SCCs). These cancers are not notifiable diseases and are not collected by the state and territory cancer registries. The diseases coded to ICD-10 codes D45-D46, D47.1 and D47.3-D47.5, which cover most of the myelodysplastic and myeloproliferative cancers, were not considered cancer at the time the ICD-10 was first published and were not routinely registered by all Australian cancer registries. The ACD contains all cases of these cancers which were diagnosed from 1982 onwards and which have been registered but the collection is not considered complete until 2003 onwards. Note that the incidence data presented are for 2006-2010 because 2011 and 2012 data for NSW and ACT were not able to be provided for the 2012 ACD. Copyright attribution: Government of the Commonwealth of Australia - Australian Institute of Health and Welfare, (2016): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.". The most recent dataset (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as "Do you have serious difficulty walking or climbing stairs?" or "Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]".

To improve the efficiency and relevance of our analysis, we removed certain attributes from the original BRFSS dataset. Many of the 279 original attributes included administrative codes, metadata, or survey-specific variables that do not contribute meaningfully to heart disease prediction—such as respondent IDs, timestamps, state-level identifiers, and detailed lifestyle questions unrelated to cardiovascular health. By focusing on a carefully selected subset of 18 attributes directly linked to medical, behavioral, and demographic factors known to influence heart health, we streamlined the dataset. This not only reduced computational complexity but also improved model interpretability and performance by eliminating noise and irrelevant information. All predicting variables could be divided into 4 broad categories:

1. Demographic factors: sex, age category (14 levels), race, BMI (Body Mass Index)

2. Diseases: weather respondent ever had such diseases as asthma, skin cancer, diabetes, stroke or kidney disease (not including kidney stones, bladder infection or incontinence)

3. Unhealthy habits:

   • Smoking - respondents that smoked at least 100 cigarettes in their entire life (5 packs = 100 cigarettes)
   • Alcohol Drinking - heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week

4. General Health:

   • Difficulty Walking - weather respondent have serious difficulty walking or climbing stairs
   • Physical Activity - adults who reported doing physical activity or exercise during the past 30 days other than their regular job
   • Sleep Time - respondent’s reported average hours of sleep in a 24-hour period
   • Physical Health - number of days being physically ill or injured (0-30 days)
   • Mental Health - number of days having bad mental health (0-30 days)
   • General Health - respondents declared their health as ’Excellent’, ’Very good’, ’Good’ ,’Fair’ or ’Poor’

Below is a description of the features collected for each patient:

IntroductionNon - melanoma skin cancer (NMSC) is a widespread malignant neoplasm affecting the skin globally. In China, over the past 30 years, the prevalence and incidence of NMSC have changed significantly, yet mortality rate (MR) data is scarce. The aim is to assess the MR data of NMSC patients worldwide from 1992 to 2021, analyze its temporal trends, and provide valuable epidemiological information for future prevention and management strategies of NMSC.MethodsUsing data from the Global Burden of Disease Study 2021 (GBD 2021), we analyzed crude mortality rate (CMR), age-standardized mortality rate (ASMR), and sex- and age-specific mortality trends, with temporal patterns assessed through longitudinal comparisons.ResultsThe MR for NMSC has shown an upward trend globally. From 1992 to 2021, both the CMR and ASMR for NMSC have increased substantially. The global ASMR has risen by approximately 30% during this period. Males have a higher ASMR compared to females, and the elderly population exhibits an accelerated and elevated ASMR trend for NMSC. In China, the mortality of NMSC is on the rise, with the current male MR exceeding that of females. Although the ASMR is projected to decline by 2030, the number of mortality cases is expected to increase, especially among males. The MR for NMSC shows a significant bias towards the elderly demographic.DiscussionThe increasing mortality of NMSC, both globally and in China, highlights the importance of effective prevention and management strategies. In addition to implementing prevention and intervention measures in susceptible populations, it is crucial to establish a screening framework for NMSC to detect minor symptoms in a timely manner. This will help in early diagnosis and potentially reduce the mortality rate associated with NMSC. Thank you for your editorial support.

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 …Show full descriptionThis 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 2015 Department of Health Primary Health Network (PHN) areas, based on 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). For further information about this dataset, please visit: Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books. Australian Cancer Database 2012 Data Quality Statement. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. 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 PHN. The number of people who could not be assigned a PHN 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). The ACD records all primary cancers except for basal and squamous cell carcinomas of the skin (BCCs and SCCs). These cancers are not notifiable diseases and are not collected by the state and territory cancer registries. The diseases coded to ICD-10 codes D45-D46, D47.1 and D47.3-D47.5, which cover most of the myelodysplastic and myeloproliferative cancers, were not considered cancer at the time the ICD-10 was first published and were not routinely registered by all Australian cancer registries. The ACD contains all cases of these cancers which were diagnosed from 1982 onwards and which have been registered but the collection is not considered complete until 2003 onwards. Note that the incidence data presented are for 2006-2010 because 2011 and 2012 data for NSW and ACT were not able to be provided for the 2012 ACD. Copyright attribution: Government of the Commonwealth of Australia - Australian Institute of Health and Welfare, (2016): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)