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Heart Disease Analysis in the U.S. (2022) - Optimizing AEDs (Automated External Defibrillators) allocation and launching targeted public service announcements.

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Python_Heart-Disease-Analysis

Heart Disease Analysis in the U.S. (2022) - Optimizing AEDs (Automated External Defibrillators) allocation and launching targeted public service announcements.

Objective

AEDs (Automated External Defibrillators) play a critical role in saving lives during cardiac emergencies. Laws and regulations requiring the presence of AEDs in public gathering places have been enacted in all fifty states in the U.S. As an data analyst in an AED supplier, my responsibility was:

  • To develop an up-to-date allocation plan for AEDs by analyzing the prevalence of heart disease in each state.
  • To identify and prioritize the most influential factors contributing to heart disease for the public service announcements.

Data Sources

Data

  • Dataset from the survey includes 40 variables associated with heart disease: gender, age category, BMI, Physical Activity as well as various pre-existing diseases such as Angina, Stroke, Diabetes etc.
  • Dataset of heart disease prevalence contains average data of gender, age groups and regions with regard to the heart disease rate during 1997-2019.

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  • Project Management: Project Brief.
  • Data: Original data and prepared data sets (Not uploaded due to file size).
  • Scripts: Python scripts of each task/step.
  • Analysis: Visualizations created with Python.

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Heart Disease Analysis in the U.S. (2022) - Optimizing AEDs (Automated External Defibrillators) allocation and launching targeted public service announcements.

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