Jiwonny29 commited on
Commit
fe829c8
1 Parent(s): 3748be6

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +34 -5
README.md CHANGED
@@ -42,7 +42,9 @@ dataset_info:
42
 
43
  <!-- Provide a quick summary of the dataset. -->
44
 
45
- This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
 
 
46
 
47
  ## Dataset Details
48
 
@@ -50,12 +52,13 @@ This dataset card aims to be a base template for new datasets. It has been gener
50
 
51
  <!-- Provide a longer summary of what this dataset is. -->
52
 
53
-
 
54
 
55
  - **Curated by:** [More Information Needed]
56
  - **Funded by [optional]:** [More Information Needed]
57
- - **Shared by [optional]:** [More Information Needed]
58
- - **Language(s) (NLP):** [More Information Needed]
59
  - **License:** [More Information Needed]
60
 
61
  ### Dataset Sources [optional]
@@ -68,7 +71,9 @@ This dataset card aims to be a base template for new datasets. It has been gener
68
 
69
  ## Uses
70
 
71
- <!-- Address questions around how the dataset is intended to be used. -->
 
 
72
 
73
  ### Direct Use
74
 
@@ -85,6 +90,30 @@ This dataset card aims to be a base template for new datasets. It has been gener
85
  ## Dataset Structure
86
 
87
  <!-- 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. -->
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
88
 
89
  [More Information Needed]
90
 
 
42
 
43
  <!-- Provide a quick summary of the dataset. -->
44
 
45
+ 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
46
+ 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
47
+ impacted the mortality rate per 100,000 individuals in the U.S.
48
 
49
  ## Dataset Details
50
 
 
52
 
53
  <!-- Provide a longer summary of what this dataset is. -->
54
 
55
+ 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
56
+ 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.
57
 
58
  - **Curated by:** [More Information Needed]
59
  - **Funded by [optional]:** [More Information Needed]
60
+ - **Shared by [optional]:** [Jiwonny29]
61
+ - **Language(s) (NLP):** [EN]
62
  - **License:** [More Information Needed]
63
 
64
  ### Dataset Sources [optional]
 
71
 
72
  ## Uses
73
 
74
+ 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.
75
+
76
+
77
 
78
  ### Direct Use
79
 
 
90
  ## Dataset Structure
91
 
92
  <!-- 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. -->
93
+ This dataset contains
94
+
95
+ - Year (int32): This column contains the year of the data record, with values ranging from 2000 to 2020
96
+ - LocationAbbr (String): Abbreviation representing the location, typically a state
97
+ - LocationDesc (String): The full name or detailed description of the location
98
+ - Latitude (float32) : Geographic coordinate that specifies the north-south position of a point on the Earth's surface
99
+ - Longitude (float32) : Geographic coordinate that specifies the east-west position of a point on the Earth's surface
100
+ - Geolocation (Tuple): A pair of latitude and longitude coordinates, formatted as (latitude, longitude), providing the geolocation or geocode of the location
101
+ - 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:
102
+ - 0: Major Cardiovascular Disease
103
+ - 1: Diseases of the Heart (Heart Disease)
104
+ - 2: Acute Myocardial Infarction (Heart Attack)
105
+ - 3: Coronary Heart Disease
106
+ - 4: Heart Failure
107
+ - 5: Cerebrovascular Disease (Stroke)
108
+ - 6: Ischemic Stroke
109
+ - 7: Hemorrhagic Stroke
110
+ - 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
111
+ - Data_Value (float32): This column represents the number of deaths per 100,000 population
112
+ - Break_Out_Category (string): This category is used for breaking down the data and includes four unique values: "Overall," "Gender," "Age," and "Race."
113
+ - 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").
114
+ - 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.
115
+ - Life_Expectancy (float32): Represents the life expectancy for the applicable year and state
116
+
117
 
118
  [More Information Needed]
119