Vizly: Analysis of Surgical Procedure Data: Percentage Distributions of Key Parameters

Vizly: AI-powered data analysis

Analysis of Surgical Procedure Data: Percentage Distributions of Key Parameters

XAN SURGICAL.xlsx

Missing file

analyze this data for in percentages for various parameters as mentioned in the chart

Show code

The data has been successfully loaded. It contains information about surgical procedures, including details such as the site of surgery, comorbidities, type of surgery, sutures used, type of suture, day of suture removal, and any complications noted.

Let's proceed to analyze this data in terms of percentages for various parameters. We will calculate the percentage distribution for the following columns:

SITE
COMORBIDITIES
TYPE OF SURGERY
SUTURES USED
TYPE OF SUTURE
DAY OF SUTURE REMOVAL
COMPLICATIONS NOTED

I will now calculate these percentages.

Show code

Here are the percentage distributions for each parameter in the dataset:

SITE:
- RIGHT UPPER EYELID: 50%
- BILATERAL UPPER EYELIDS: 50%
COMORBIDITIES:
- HYPOTHYROIDISM: 50%
- None (-): 50%
TYPE OF SURGERY:
- EXCISION AND CLOSURE: 100%
SUTURES USED:
- POLYPROPELENE 6-0: 100%
TYPE OF SUTURE:
- SIMPLE INTERRUPTED: 100%
DAY OF SUTURE REMOVAL:
- Day 7: 50%
- Day 8: 50%
COMPLICATIONS NOTED:
- No complications were noted in the dataset.

These percentages give us an overview of the distribution of different parameters in the surgical data. If you have any further questions or need additional analysis, feel free to ask!

Can you visualize these percentages in a chart?

Show code

Can you help me with further statistical analysis?

Certainly! I can assist you with various types of statistical analysis. Here are some options you might consider:

Descriptive Statistics: Calculate measures such as mean, median, mode, standard deviation, etc.
Correlation Analysis: Determine relationships between different variables.
Hypothesis Testing: Perform tests like t-test, chi-square test, etc.
Regression Analysis: Explore relationships between dependent and independent variables.
ANOVA: Analyze differences among group means.
Time Series Analysis: If you have time-related data, we can perform trend analysis.

Please let me know which type of analysis you are interested in, or if you have a specific question in mind!

Can you calculate descriptive statistics for the dataset?

Show code

Here are the descriptive statistics for the dataset:

Numerical Columns:

DAY OF SUTURE REMOVAL:
- Count: 2
- Mean: 7.5
- Standard Deviation: 0.71
- Minimum: 7
- 25th Percentile: 7.25
- Median (50th Percentile): 7.5
- 75th Percentile: 7.75
- Maximum: 8

(Note: There are no numerical values for "COMPLICATIONS NOTED" as it contains only NaN values.)

Categorical Columns:

SITE:
- Unique Values: 2
- Most Frequent: RIGHT UPPER EYELID (1 occurrence)
COMORBIDITIES:
- Unique Values: 2
- Most Frequent: HYPOTHYROIDISM (1 occurrence)
TYPE OF SURGERY:
- Unique Values: 1
- Most Frequent: EXCISION AND CLOSURE (2 occurrences)
SUTURES USED:
- Unique Values: 1
- Most Frequent: POLYPROPELENE 6-0 (2 occurrences)
TYPE OF SUTURE:
- Unique Values: 1
- Most Frequent: SIMPLE INTERRUPTED (2 occurrences)

These statistics provide an overview of the dataset's distribution and central tendencies. If you need further analysis or have specific questions, feel free to ask!

Can you visualize the descriptive statistics?

Show code

Can you perform correlation analysis on the dataset?

Show code

Can you explain the correlation results?

The correlation matrix provides a measure of the linear relationship between numerical variables in the dataset. Here's a brief explanation of the results:

Correlation Coefficient: This value ranges from -1 to 1.
- A value of 1 indicates a perfect positive linear relationship.
- A value of -1 indicates a perfect negative linear relationship.
- A value of 0 indicates no linear relationship.

In this dataset, the correlation matrix only includes the numerical column DAY OF SUTURE REMOVAL. Since there are no other numerical columns to compare it with, the correlation matrix is essentially a 1x1 matrix with a value of 1, indicating a perfect correlation with itself.

If you have more numerical data or would like to explore correlations with categorical data, we can encode categorical variables and perform further analysis. Let me know if you would like to proceed with that or have any other questions!