Datasets:

Jiwonny29
/

project1

@@ -38,58 +38,32 @@ dataset_info:
       dtype: float32
 ---
-# Dataset Card for Dataset Name
-<!-- Provide a quick summary of the dataset. -->
-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
-<!-- Provide a longer summary of what this dataset is. -->
-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]
-- **License:** [More Information Needed]
-### Dataset Sources [optional]
-<!-- Provide the basic links for the dataset. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## 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
-<!-- This section describes suitable use cases for the dataset. -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-[More Information Needed]
 ## Dataset Structure
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
 This dataset contains
 - Year (int32): This column contains the year of the data record, with values ranging from 2000 to 2020
@@ -107,100 +81,10 @@ This dataset contains
   - 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
-[More Information Needed]
-## Dataset Creation
-### Curation Rationale
-<!-- Motivation for the creation of this dataset. -->
-[More Information Needed]
-### Source Data
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-#### Data Collection and Processing
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-[More Information Needed]
-#### Who are the source data producers?
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-[More Information Needed]
-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### Annotation process
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-[More Information Needed]
-#### Who are the annotators?
-<!-- This section describes the people or systems who created the annotations. -->
-[More Information Needed]
-#### Personal and Sensitive Information
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Dataset Card Authors [optional]
-[More Information Needed]
-## Dataset Card Contact
-[More Information Needed]

       dtype: float32
 ---
 ### Dataset Description
+This dataset encompasses mortality rates for cardiovascular (CVD) and heart diseases across the United States, covering both state-specific and national levels, from 2000 to 2020. The mortality rate is quantified as the number of deaths per 100,000 individuals annually in the US. The dataset is structured to classify mortality rates according to various demographic factors, including overall rates, gender (female, male), race (white, black, Hispanic, other), and age groups (18-24, 25-44, 45-65, 65+). Additionally, life expectancy data for each state is incorporated in the dataset. For ease of use, I combined the data on a five-year interval rather than an annual basis.
+### Dataset Sources
+- CVD Mortality Data: Centers for Disease Control and Prevention(CDC) National Vital Statistics System
+   - https://data.cdc.gov/Heart-Disease-Stroke-Prevention/National-Vital-Statistics-System-NVSS-National-Car/kztq-p2jf/about_data
+- Life Expectancy Data: Institute for Health Metrics and Evaluation
+   - https://ghdx.healthdata.org/record/ihme-data/united-states-life-expectancy-by-county-race-ethnicity-2000-2019
 ## Uses
+This dataset serves as a valuable resource for researchers and individuals interested in examining and identifying patterns related to cardiovascular diseases in the United States. It can be utilized to forecast future fatalities caused by heart diseases by leveraging similar features present in the dataset. Additionally, the dataset enables users to gain insights into identifying states that require assistance and support in reducing mortality rates. Below are example use cases and corresponding codes:
+- Analyzing the comprehensive picture of mortality and conducting time series analysis on mortality rates
+   - https://colab.research.google.com/drive/1ulygrSt9jt3x_4WIGD6QdK0TcGZlpuYF
+- Building regression models
+   - https://colab.research.google.com/drive/1DhIni026qz5qqjfWwKXnqoQXDy-HzroC
+- Developing a web application for users to quickly understand and compare mortality rates among states, along with relevant information like state population.
+   - https://github.com/jiwonny29/Exploring_US_Cardiovascular_Mortality_Trends_via_Streamlit
 ## Dataset Structure
 This dataset contains
 - Year (int32): This column contains the year of the data record, with values ranging from 2000 to 2020
   - 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 0, 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