A Visual Analysis of Patient Data : Blood Pressure and Medical Assessments

Hello all ✋

 In this blog post, we'll be looking into a dataset obtained from a local hospital. This dataset contains blood pressure readings, evaluations by both general and external doctors, and the final decision regarding immediate care for patients. Our aim is to visually analyze the connection between blood pressure and medical assessments. To accomplish this, we'll be using side-by-side boxplots and histograms to identify patterns and trends within the data.

To begin our analysis, we need to prepare the data and generate visualizations as in the URL given below. It will create side-by-side boxplots for general and external doctor assessments, along with a histogram showing the overall distribution of blood pressure values. The x-axis represents the assessment (bad or good), while the y-axis represents the blood pressure values.

  Patient Health Assessment

The boxplot displays:


First Assessment (Bad vs. Good): Patients assessed as 'Bad' have a wider range of BP values, indicating potentially severe health issues compared to those assessed as 'Good' by the initial doctor. Second Assessment (Low vs. High): After the second assessment, the BP values are distinctly divided. Higher ratings now indicate higher median BP, implying more severe conditions. 

Final Decision (Low vs. High): It is worth noting that the decision to provide immediate care is largely based on the patient's blood pressure levels. Patients with higher blood pressure levels are usually considered to require urgent attention, while those with lower levels may not be in immediate danger. Moreover, patients who are assessed as 'Bad' have a wider range of blood pressure values, which suggests that their health condition may be more critical than those assessed as 'Good' by the initial doctor.

Histogram Analysis:



The histogram of blood pressure measurements for all patients shows a wide range of values, with a significant number of cases in the lower to mid blood pressure range. However, there are some extreme cases on the high end, indicating patients with severely elevated blood pressure levels. This distribution emphasizes the diversity in patient conditions, demonstrating the presence of both typical cases and those requiring urgent intervention.

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