In 2022, the incidence of lung cancer cases among those aged above 75 years of age in the European Union was ***** per 100,000 men and ***** per 100,000 women. The risk of developing lung cancer can increase by smoking, inhaling second hand smoke and exposure to asbestos

Lung Cancer Dataset

Introduction:

Lung cancer remains one of the most prevalent and deadly forms of cancer worldwide, posing significant challenges for early detection and effective treatment. To contribute to the global effort in understanding and combating this disease, we are excited to introduce our comprehensive Lung Cancer Dataset, now available on Kaggle.

Scientific Overview:

This dataset is an invaluable asset in the realm of Health Care, providing a structured foundation for the development of cancer detection models. This dataset exemplifies the variety of symptoms of Lung Cancer. Each category within the dataset—'GENDER', 'AGE', 'SMOKING', 'YELLOW_FINGERS', 'ANXIETY', 'PEER_PRESSURE', 'CHRONIC_DISEASE', 'FATIGUE', 'ALLERGY', 'WHEEZING', 'ALCOHOL_CONSUMING', 'COUGHING', 'SHORTNESS_OF_BREATH', 'SWALLOWING_DIFFICULTY', 'CHEST_PAIN'—has been carefully curated to encompass a diverse range of symptoms, ensuring that the resulting models are versatile and accurate. This scientific approach not only enhances the dataset's diversity to record symptoms of lung cancer but also contributes to the broader field of AI-driven health technologies, pushing the boundaries of what health care assistants can achieve.

Dataset Composition

The Lung Cancer Dataset includes a diverse array of symptoms essential for comprehensive analysis and model development. The primary categories of data are as follows:

1. Patient Demographics

Age: Provides the age at diagnosis, enabling analysis of age-related incidence and outcomes. Gender: Includes information on patient gender, facilitating gender-based studies. Smoking Status: Categorized as current smoker, former smoker, or non-smoker, this data is critical for evaluating the impact of smoking on lung cancer risk and progression.

2. Medical History

Comorbidities: Details additional health issues such as chronic obstructive pulmonary disease (COPD), which are relevant for treatment planning and prognosis.

3. Clinical Data

Vital Signs: Records of blood pressure, heart rate, respiratory rate, and other vital signs at diagnosis and during treatment.

Implementation Guide for the Mental Health Dataset:

Data Integration

Dataset Acquisition: Obtain the Lung Cancer Dataset. Data Exploration: Familiarize yourself with the structure and contents of the dataset, including symptoms and conclusions related to different conditions.

Preprocessing

Data Cleaning: Remove any irrelevant or redundant entries, and ensure consistency in formatting across the dataset. Tokenization: Break down the symptoms and conclusions into tokens or individual words to facilitate analysis and model training. Normalization: Standardize the text data by converting it to lowercase and removing punctuation or special characters as needed.

Model Training

Choose a Framework: Select a suitable machine learning or natural language processing framework such as TensorFlow, PyTorch, or spaCy. Model Selection: Decide on the type of model to use, such as recurrent neural networks (RNNs), transformers, or sequence-to-sequence models, based on the complexity of the dataset and the desired level of accuracy. Training Process: Train the chosen model using the preprocessed dataset, adjusting hyperparameters as necessary to optimize performance. Evaluation: Assess the performance of the trained model using appropriate metrics such as accuracy, precision, recall, and F1-score.

Deployment

Integration: Integrate the trained model into a chatbot or virtual assistant application using programming languages like Python or JavaScript. User Interface Design: Design an intuitive user interface that allows users to interact with the chatbot and receive responses related to Lung Cancer. Testing: Conduct thorough testing of the deployed chatbot to ensure functionality, accuracy, and responsiveness in providing relevant result. Feedback Mechanism: Implement a feedback mechanism to gather user feedback and improve the chatbot's performance over time.

Continuous Improvement

Monitoring: Continuously monitor the chatbot's performance and user interactions to identify areas for improvement. Data Updates: Periodically update the dataset with new symptoms to ensure accuracy. Model Refinement: Fine-tune the model based on user feedback and additional training data to enhance the chatbot's effectiveness and accuracy in detecting lung cancer. By following this implementation guide, developers can effectively leverage the Lung Cancer Dataset to build and deploy AI-driven chatbots and virtual assistants that offer accurate predictions to users worldwide.

Potential Applications

The extensive nature of the Lung Cancer Dataset supports a wide range of scientific and clinical applications:

Machine Learning Models: Facilitates the development of predictive algorithms for early detection, prognosis, and personalized t...

As of 2022, the age-standardized incidence rate of lung cancer worldwide was 23.6 per 100,000 population. At this time, the incidence rate of lung cancer was highest in Eastern Asia. This statistic shows the age-standardized incidence rate of lung cancer worldwide as of 2022, by region.

Abstract Objective: To identify the socioepidemiologic and histopathologic patterns of lung cancer patients in the Middle Euphrates region. Patients and Methods: This study analyzed medical information from lung cancer patients at the Middle Euphrates Cancer Center in Iraq from January 2018 to December 2023. Demographic information (age, gender, residency, and education level) as well as clinical details (histopathological categorization) were obtained. The inclusion criteria included all confirmed lung cancer cases, while cases with inadequate data or non-lung cancer diagnosis were omitted. The data were analyzed using IBM SPSS Statistics (version 26). The data summarized using descriptive statistics, and chi-square tests used to identify correlations between categorical variables at a significance level of p < 0.05. Ethical approval was obtained from the relevant institutional review board. Results: A total of 1162 patients were included with mean age at diagnosis(64.47±11.45) years. Majority of patients are over 60 years (64.4%), followed by (40–60 years), 34%, and the least affected group is under 40 years (1.6%). Males account for the majority of cases (68%), while females about 32%, with male:female ratio that fluctuate around 2:1. Illiterate patients and those with low education levels represent the largest proportion accounting for about 87.9% of the study population. Squamous Cell Carcinoma (SCC) is the most frequent subtype (41.7%), followed closely by Adenocarcinoma (AC) at 37%, and Small Cell Lung Cancer (SCLC), 10.5%. Although SCC is the predominant subtype overall, AC incidence is increasing overtime (from 31.7% in 2018 to 41.4% in 2023) with predominance in females, younger and higher educated groups. While the percentage of SCLC and other less common subgroups remained relatively stable over time, there is a significant reduction in NSCLC-NOS diagnoses (from 11.1% in 2018 to 3.2% in 2023). Conclusions: In Iraq, specifically in the Middle Euphrates region, lung cancer is a major public health issue in the elder age groups. The two main subtypes, SCC and AC, are the main contributors, with obvious increment in AC cases in the recent years. The shifting trends indicate the urgent need for improved screening strategies, focused preventative initiatives, and customized treatment plans in view of changing risk profiles.

This dataset provides valuable insights into lung cancer cases, risk factors, smoking trends, and healthcare access across 25 of the world's most populated countries. It includes 220,632 individuals with details on their age, gender, smoking history, cancer diagnosis, environmental exposure, and survival rates. The dataset is useful for medical research, predictive modeling, and policy-making to understand lung cancer patterns globally.

As of 2022, the age-standardized incidence rate of lung cancer among males in Polynesia was 54.7 per 100,000 population, the highest rate worldwide. The incidence rate of lung cancer among females was highest in Northern America. This statistic shows the age-standardized incidence rate of lung cancer worldwide as of 2022, by region and gender.

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!

This dataset contains Cancer Incidence data for Lung Cancer (All Stages^) including: Age-Adjusted Rate, Confidence Interval, Average Annual Count, and Trend field information for US States for the average 5 year span from 2016 to 2020.Data are segmented by sex (Both Sexes, Male, and Female) and age (All Ages, Ages Under 50, Ages 50 & Over, Ages Under 65, and Ages 65 & Over), with field names and aliases describing the sex and age group tabulated.For more information, visit statecancerprofiles.cancer.govData NotationsState Cancer Registries may provide more current or more local data.TrendRising when 95% confidence interval of average annual percent change is above 0.Stable when 95% confidence interval of average annual percent change includes 0.Falling when 95% confidence interval of average annual percent change is below 0.† Incidence rates (cases per 100,000 population per year) are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84, 85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used for SEER and NPCR incidence rates.‡ Incidence Trend data come from different sources. Due to different years of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. Please refer to the source for each area for additional information.Rates and trends are computed using different standards for malignancy. For more information see malignant.^ All Stages refers to any stage in the Surveillance, Epidemiology, and End Results (SEER) summary stage.Data Source Field Key(1) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(5) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(6) Source: National Program of Cancer Registries SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention (based on the 2022 submission).(7) Source: SEER November 2022 submission.(8) Source: Incidence data provided by the SEER Program. AAPCs are calculated by the Joinpoint Regression Program and are based on APCs. Data are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84,85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used with SEER November 2022 data.Some data are not available, see Data Not Available for combinations of geography, cancer site, age, and race/ethnicity.Data for the United States does not include data from Nevada.Data for the United States does not include Puerto Rico.

In 2023, the projected incidence rate of lung cancer in the Australian population was around ***** cases per 100,000 in the 75 to 79 year old age group, an incidence rate higher than any other age group. The lung cancer incidence rate was projected to be above *** cases per 100,000 for all age groups over **.

In 2022, 83.2 males and 69.3 females per 100,000 population in England were registered as newly diagnosed with malignant neoplasm of bronchus and lung. Over the analyzed years, the rate of newly diagnosed cases for male individuals has seen a decrease trend. Conversely, the rate of newly diagnosed cases for females has seen a steady increase over the years. This statistic shows the rate of newly diagnosed cases of lung cancer per 100,000 population in England from 1995 to 2022, by gender.

Legacy unique identifier: P00513

BackgroundBetter information on lung cancer occurrence in lifelong nonsmokers is needed to understand gender and racial disparities and to examine how factors other than active smoking influence risk in different time periods and geographic regions. Methods and FindingsWe pooled information on lung cancer incidence and/or death rates among self-reported never-smokers from 13 large cohort studies, representing over 630,000 and 1.8 million persons for incidence and mortality, respectively. We also abstracted population-based data for women from 22 cancer registries and ten countries in time periods and geographic regions where few women smoked. Our main findings were: (1) Men had higher death rates from lung cancer than women in all age and racial groups studied; (2) male and female incidence rates were similar when standardized across all ages 40+ y, albeit with some variation by age; (3) African Americans and Asians living in Korea and Japan (but not in the US) had higher death rates from lung cancer than individuals of European descent; (4) no temporal trends were seen when comparing incidence and death rates among US women age 40–69 y during the 1930s to contemporary populations where few women smoke, or in temporal comparisons of never-smokers in two large American Cancer Society cohorts from 1959 to 2004; and (5) lung cancer incidence rates were higher and more variable among women in East Asia than in other geographic areas with low female smoking. ConclusionsThese comprehensive analyses support claims that the death rate from lung cancer among never-smokers is higher in men than in women, and in African Americans and Asians residing in Asia than in individuals of European descent, but contradict assertions that risk is increasing or that women have a higher incidence rate than men. Further research is needed on the high and variable lung cancer rates among women in Pacific Rim countries.

This dataset contains information on lung cancer risk factors across various countries, focusing on demographic details, smoking behaviors, and family history. Researchers and public health professionals can use this data to study patterns of lung cancer incidence, identify trends related to smoking and passive smoking exposure, and assess the impact of family history on lung cancer risk.

What You Can Perform with This Data:

Risk Factor Analysis: Analyze how smoking habits, exposure to secondhand smoke, and family history correlate with lung cancer risk. Comparative Study: Compare lung cancer risk factors across different countries and regions. Demographic Insights: Explore how age and gender impact the prevalence of lung cancer risk factors. Statistical Modeling: Build models to predict lung cancer risk based on various factors such as smoking history, exposure to passive smoke, and genetic predisposition. Public Health Research: Identify populations with high-risk behaviors and suggest interventions or preventive measures.

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.

As of 2019, the age-standardized incidence rate for lung cancer in males was highest among the colored population group, with ***** cases reported per 100,000 population. This is a significant drop compared to 2018, which reported ***** incidences and ***** deaths per 100,000 population.

Deaths from lung cancer - Directly age-Standardised Rates (DSR) per 100,000 population Source: Office for National Statistics (ONS) Publisher: Information Centre (IC) - Clinical and Health Outcomes Knowledge Base Geographies: Local Authority District (LAD), Government Office Region (GOR), National, Primary Care Trust (PCT), Strategic Health Authority (SHA) Geographic coverage: England Time coverage: 2005-07, 2007 Type of data: Administrative data

Summary statistics of average lung cancer incidence rates and average daily smokers in percentage in 8 U.S. geographic regions, 1999–2012.

Lung Cancer Deaths reports the number, crude rate, and age-adjusted mortality rate (AAMR) of deaths due to lung cancer.

Data Showing - Age rates of lung cancer incidence - Plymouth - 2009 - 2013 Data and Resources Age rates of lung cancer incidence in Plymouth 2009-2013 .csv Age rates of lung cancer incidence in Plymouth 2009-2013

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.