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Week 4: Analyzing Patient Blood Pressure and Medical Ratings

 Week 4: Analyzing Patient Blood Pressure and Medical Ratings





In this week’s assignment, I worked with a dataset collected by a local hospital that includes various observations from eight patients. The data consists of five variables: frequency of hospital visits (Freq), blood pressure measurements (bloodp), first assessment by a general doctor (visit_1), second assessment by an external doctor (visit_2), and the final decision regarding immediate care (visit_3).

Data Overview

The dataset features a mix of patient blood pressure readings and medical assessments. The "first" and "second" columns indicate the ratings from the doctors (with "bad" coded as 1 and "good" as 0), while the "finaldecision" column reflects the emergency unit’s decision on the patients' care (low = 0, high = 1).


Data Visualization

I created a side-by-side boxplot and a histogram to visualize the blood pressure data. The histogram illustrates the distribution of blood pressure readings among the patients, revealing that most patients have blood pressure values below 120, which is generally considered normal. However, there are outliers with significantly higher readings, particularly one patient with a blood pressure of 205.

The side-by-side boxplots provide insights into how the doctor assessments correlate with blood pressure measurements. Comparing the boxplots for the first and second assessments, it appears that patients rated "bad" tend to have higher blood pressure readings. This suggests a potential relationship between higher blood pressure and negative medical assessments.


Insights from the Graphs

The visualizations I created provide valuable insights into the relationship between blood pressure and medical assessments from the doctors.

Histogram Insights

The histogram displays the distribution of blood pressure readings among the patients. Key observations include:

  • Distribution Shape: Most blood pressure values are concentrated in the lower range, below 120 mmHg, indicating that a majority of patients have normal to slightly elevated blood pressure. This is promising as it suggests that most patients are in a healthy range.

  • Outliers: There is a notable outlier with a blood pressure reading of 205 mmHg. This extreme value could represent a patient in critical condition and emphasizes the need for immediate medical attention. The presence of such outliers in the dataset highlights the importance of continuous monitoring and assessment in clinical settings.

Boxplot Insights

The side-by-side boxplots provide a comparative view of blood pressure based on the assessments made by the doctors:

  • First vs. Second Assessments: The boxplots for the "first" and "second" assessments show that patients rated as "bad" tend to have higher blood pressure readings. This suggests a potential correlation between higher blood pressure and more negative evaluations by medical professionals. It raises questions about whether high blood pressure influences a doctor’s assessment or if higher readings are a reflection of underlying health issues.

  • Final Decision Correlation: By comparing the boxplots with the final decision variable, we can infer that patients with higher blood pressure are more likely to receive a "high" care decision from the emergency unit. This observation aligns with the expectation that elevated blood pressure may necessitate more immediate and intensive medical interventions.


References and Further Exploration

To enhance my understanding and analysis of the dataset, I referred to several informative YouTube videos that provided valuable insights into data visualization and statistical analysis in R. The videos helped clarify key concepts and techniques that I applied in my project. You can check them out here: Video 1, Video 2, and Video 3. Additionally, I have uploaded the complete code and data analysis on my GitHub repository, which you can access here. This repository contains the scripts used for visualizations, and you can explore the data further.

Conclusion

Overall, these visualizations underscore the critical relationship between blood pressure and medical assessments. They highlight the importance of thorough evaluations and the potential need for intervention for patients with elevated readings. Moving forward, it would be beneficial to explore additional variables, such as patient demographics and other health indicators, to gain a more comprehensive understanding of the factors influencing blood pressure and medical outcomes.




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