{
    "data_id": "45953",
    "name": "Death_by_various_risk_factors",
    "exact_name": "Death_by_various_risk_factors",
    "version": 1,
    "version_label": null,
    "description": "Description:\nThe \"deathsbyriskfactor_new.csv\" dataset encompasses a comprehensive collection of global health data focusing on deaths attributed to various risk factors across different countries. It provides detailed insights into the impact of dietary habits, environmental factors, and personal health conditions on mortality rates for both sexes of all ages, presenting a critical resource for research in public health, epidemiology, and policy-making. The dataset spans various years and includes data for countries such as Haiti, Bangladesh, Italy, Morocco, and Ethiopia, among others, offering a broad perspective on global health issues.\n\nAttribute Description:\n- Entity: The country of the data point (e.g., Haiti, Bangladesh).\n- Code: Country code (e.g., TTO for Trinidad and Tobago, AUS for Australia).\n- Year: The year of the data record (ranging from 1992 to 2018).\n- Deaths attributed to numerous risk factors such as high systolic blood pressure, diet high in sodium, diet low in whole grains, alcohol use, diet low in fruits, unsafe water source, secondhand smoke, low birth weight, child wasting, unsafe sex, diet low in nuts and seeds, household air pollution from solid fuels, diet low in vegetables, smoking, high fasting plasma glucose, air pollution, high body-mass index, unsafe sanitation, drug use, low bone mineral density, vitamin A deficiency, child stunting, non-exclusive breastfeeding, iron deficiency, ambient particulate matter pollution, low physical activity, no access to handwashing facility, high LDL cholesterol.\n\nUse Case:\nThis dataset is invaluable for public health research and analysis, aiming to assess and compare the impacts of various risk factors on mortality across different countries and years. It can support the development of evidence-based health policies, interventions, and preventive measures tailored to specific risk factors and their demographic impacts. Researchers, policymakers, and public health professionals can utilize this dataset to identify trends, prioritize healthcare objectives, and allocate resources effectively to mitigate the most harmful risk factors in targeted populations.",
    "format": "arff",
    "uploader": "Egor Karasev",
    "uploader_id": 42279,
    "visibility": "public",
    "creator": "\"Iwo Godzwon\"",
    "contributor": "\"None\"",
    "date": "2024-05-06 16:16:23",
    "update_comment": null,
    "last_update": "2024-05-06 16:16:23",
    "licence": "Public Domain (CC0)",
    "status": "in_preparation",
    "error_message": null,
    "url": "https:\/\/api.openml.org\/data\/download\/22120396\/dataset",
    "default_target_attribute": null,
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "Death_by_various_risk_factors",
            "Description: The \"deathsbyriskfactor_new.csv\" dataset encompasses a comprehensive collection of global health data focusing on deaths attributed to various risk factors across different countries. It provides detailed insights into the impact of dietary habits, environmental factors, and personal health conditions on mortality rates for both sexes of all ages, presenting a critical resource for research in public health, epidemiology, and policy-making. The dataset spans various years and includ "
        ],
        "weight": 5
    },
    "qualities": [],
    "tags": [],
    "features": [],
    "nr_of_issues": 0,
    "nr_of_downvotes": 0,
    "nr_of_likes": 0,
    "nr_of_downloads": 0,
    "total_downloads": 0,
    "reach": 0,
    "reuse": 0,
    "impact_of_reuse": 0,
    "reach_of_reuse": 0,
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}