metadata

license: apache-2.0
task_categories:
  - feature-extraction
language:
  - en
tags:
  - biology
pretty_name: test
size_categories:
  - 100K<n<1M
dataset_info:
  config_name: mydata
  features:
    - name: Year
      dtype: int32
    - name: LocationAbbr
      dtype: string
    - name: LocationDesc
      dtype: string
    - name: Latitude
      dtype: float32
    - name: Longitude
      dtype: float32
    - name: Disease_Type
      dtype: int32
    - name: Data_Value_Type
      dtype: int32
    - name: Data_Value
      dtype: float32
    - name: Break_Out_Category
      dtype: string
    - name: Break_Out_Details
      dtype: string
    - name: Break_Out_Type
      dtype: int32
    - name: Life_Expectancy
      dtype: float32

Dataset Card for Dataset Name

This dataset card is designed for individuals who want to perform time series prediction analysis on cardiovascular disease statistics in the United States. It offers real data-driven insights into patterns and trends according to regional, gender, ethnic, and life expectancy factors. Users can utilize this dataset to comprehend various types of cardiovascular diseases that have historically impacted the mortality rate per 100,000 individuals in the U.S.

Dataset Details

Dataset Description

The Cardiovascular Surveillance statistics and average life expectancy datasets for each U.S. state have been combined to enhance the depth of analysis for researchers. This merged dataset uses life expectancy in each state as a metric for the mortality rate. It highlights specific states that may require more intensive monitoring, care, or emergency support for cardiovascular patients at risk of death.

Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [Jiwonny29]
Language(s) (NLP): [EN]
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Uses

The applications of this dataset are diverse, depending on the user's objectives. Firstly, it can be utilized for data analysis and regression prediction. This includes conducting regression analyses of mortality rates for different groups (gender, age, race/ethnicity) per 100,000 population, broken down by region. Secondly, it supports Regional Clustering and Healthcare Evaluation. This involves grouping regions by state within the United States and assessing the consistency or variation in cardiovascular support and healthcare services, including emergency systems, across these regions. Additionally, the dataset facilitates Statistical Comparison and Insights. Users can perform statistical comparisons to pinpoint which demographic groups in each region are most in need of cardiovascular assistance. With these insights, targeted improvements can be made in the states where such assistance is most urgently required.

Direct Use

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Out-of-Scope Use

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Dataset Structure

This dataset contains

Year (int32): This column contains the year of the data record, with values ranging from 2000 to 2020
LocationAbbr (String): Abbreviation representing the location, typically a state
LocationDesc (String): The full name or detailed description of the location
Latitude (float32) : Geographic coordinate that specifies the north-south position of a point on the Earth's surface
Longitude (float32) : Geographic coordinate that specifies the east-west position of a point on the Earth's surface
Geolocation (Tuple): A pair of latitude and longitude coordinates, formatted as (latitude, longitude), providing the geolocation or geocode of the location
Disease_Type (int32): A key column in the dataset, representing eight unique types of cardiovascular diseases, numbered from 0 to 7. The values correspond to the following diseases:
- 0: Major Cardiovascular Disease
- 1: Diseases of the Heart (Heart Disease)
- 2: Acute Myocardial Infarction (Heart Attack)
- 3: Coronary Heart Disease
- 4: Heart Failure
- 5: Cerebrovascular Disease (Stroke)
- 6: Ischemic Stroke
- 7: Hemorrhagic Stroke
Data_Value_Type (int32): Represents the type of data value. "Age-Standardized" is represented by 1, and "Crude" is represented by 2, indicating the measurement methods for the data value columns
Data_Value (float32): This column represents the number of deaths per 100,000 population
Break_Out_Category (string): This category is used for breaking down the data and includes four unique values: "Overall," "Gender," "Age," and "Race."
Break_Out_Details (string): Specific subcategories within the Break_Out_Category. This column includes values like "Overall," six age categories (e.g., "18-24," "25-44"), two gender categories (e.g., "Female," "Male"), and four race categories (e.g., "Hispanic," "Non-Hispanic Black," "Non-Hispanic White," "Other").
Break_Out_Type (int32): A numerical transformation of the Break_Out_Details column. In this system, "Overall" is represented as 0, "Male" and "Female" as 1 and 2, respectively; age groups "18-24," "25-44," "45-64," "65+" as 1, 2, 3, 4, respectively; and racial categories "Non-Hispanic White," "Non-Hispanic Black," "Hispanic," "Other" as 1, 2, 3, 4, respectively.
Life_Expectancy (float32): Represents the life expectancy for the applicable year and state

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Dataset Creation

Curation Rationale

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Source Data

Data Collection and Processing

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Who are the source data producers?

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Annotation process

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Personal and Sensitive Information

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Bias, Risks, and Limitations

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Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

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