Medical Data Analyzer

This tool is designed to facilitate the analysis and visualization of medical datasets using Python. The project was created and tested on Jupyter Notebook.

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Features

• Data Cleaning: Handles missing values and outliers in medical datasets.
• Exploratory Data Analysis (EDA): Generates summary statistics, correlations, and visualizations.
• Custom Visualizations: Create detailed plots such as histograms, heatmaps, and category plot.
• Correlation Analysis: Calculate and visualize correlation matrices to identify relationships between variables.
• Data Validation: Filters out inconsistent data, such as cases where diastolic pressure is higher than systolic.
• Disease Analysis: Differentiates between the presence and absence of cardiovascular disease.

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Parameters

The analysis is based on the following parameters:

• id
• age
• sex
• height
• weight
• ap_hi (systolic blood pressure)
• ap_lo (diastolic blood pressure)
• cholesterol
• gluc (glucose level)
• smoke
• alco (alcohol consumption)
• active (physical activity)
• cardio (cardiovascular disease indicator)

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Getting Started

Prerequisites

Ensure you have the following installed on your system:

• Python 3.7+
• Jupyter Notebook
• Required Python libraries:

  pandas
  numpy
  matplotlib
  seaborn
  scikit-learn

  Install these libraries using pip:

  pip install pandas numpy matplotlib seaborn scikit-learn

Installation

1. Clone the repository:

   git clone https://github.com/AyobamiMichael/medicaldata_analysis.git

2. Navigate to the project directory:

   cd medical_data_analysis

3. Open the project in Jupyter Notebook:

   jupyter notebook

4. Launch the main analysis notebook: medical_data_visualizer.ipynb.

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Usage

1. Load Data: Upload your medical dataset in .csv format.
2. Clean Data:
   • Filter out patient segments where diastolic pressure (ap_lo) is higher than systolic pressure (ap_hi).
   • Remove or handle other inconsistent or missing data as needed.
3. Run Analysis:
   • Perform exploratory data analysis to examine relationships between parameters.
   • Calculate the correlation matrix.
   • Generate a heatmap to visualize correlations.
   • Differentiate between the presence and absence of cardiovascular disease (cardio).
4. Save Results: Export cleaned datasets and visualizations.

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Example

Here's a snippet of the data cleaning and analysis process:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load the dataset
data = pd.read_csv('medical_examination.csv')

# Filter out invalid data (diastolic > systolic)
data = data[data['ap_lo'] <= data['ap_hi']]

# Calculate the correlation matrix
corr_matrix = data.corr()

# Generate a heatmap
sns.heatmap(corr_matrix, annot=True, fmt='.2f', cmap='coolwarm')
plt.title('Correlation Matrix Heatmap')
plt.show()

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Contributing

Contributions are welcome! Follow these steps to contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add new feature"

4. Push to the branch:

   git push origin feature-name

5. Submit a pull request.

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License

This project is licensed under the MIT License.

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Contact

For any inquiries or feedback, please contact:

• Author: Ayobami Michael Opefeyijimi
• Email: ayobamiwealth@gmail.com

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Thank you.