https://github.com/robinmillford/optimizing-treatment-plans-through-data-analysis

The primary focus was on understanding customer health, treatment, and associated charges over multiple years.
https://github.com/robinmillford/optimizing-treatment-plans-through-data-analysis

data-analysis data-visualization healthcare mysql powerbi sql

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The primary focus was on understanding customer health, treatment, and associated charges over multiple years.

• Host: GitHub
• URL: https://github.com/robinmillford/optimizing-treatment-plans-through-data-analysis
• Owner: RobinMillford
• Created: 2024-01-14T10:09:59.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-01-14T10:16:15.000Z (10 months ago)
• Last Synced: 2024-01-14T16:19:46.922Z (10 months ago)
• Topics: data-analysis, data-visualization, healthcare, mysql, powerbi, sql
• Language: Jupyter Notebook
• Homepage:
• Size: 6.21 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
**Project Title: Medical Data Analysis and Visualization using SQL and Power BI**

**Summary:**

In this project, I conducted a comprehensive analysis of medical data using SQL queries on two tables, 'hospitalisation' and 'medical_examinations.' The primary focus was on understanding customer health, treatment, and associated charges over multiple years.

1. **Data Exploration and Cleaning:**

   - Identified and handled null values, checked data types, and removed duplicates.

   - Renamed and cleaned columns for clarity and consistency.

2. **Medical Data Analysis:**

   - Conducted various SQL queries to extract valuable insights:

     - Calculated averages, totals, and percentages.

     - Identified customers with specific health conditions.

     - Analyzed trends in BMI, charges, and major surgeries.

   

3. **Complex Analyses:**

   - Implemented more advanced queries:

     - Leveraged Common Table Expressions (CTEs) for yearly averages and hospitalization counts.

     - Used window functions for cumulative charges and ranking.

     - Conducted analyses on fluctuating BMI levels and consistent frequent treatments.

4. **Power BI Visualization:**

   - The SQL insights were complemented by visualizations created in Power BI.

   - Utilized Power BI's interactive dashboards and reports for a more intuitive understanding of the data.

   - Visualized trends, relationships, and key metrics, enhancing the interpretability of the analysis.

     Dashboard 1 - ![Alt Text](https://github.com/RobinMillford/Optimizing-Treatment-Plans-Through-Data-Analysis/blob/main/Dashboard%201.png)

     Dashboard 2 - ![Alt Text](https://github.com/RobinMillford/Optimizing-Treatment-Plans-Through-Data-Analysis/blob/main/Dashboard%202.png)

     Dashboard 3 - ![Alt Text](https://github.com/RobinMillford/Optimizing-Treatment-Plans-Through-Data-Analysis/blob/main/Dashboard%203.png)

     Dashboard 4 - ![Alt Text](https://github.com/RobinMillford/Optimizing-Treatment-Plans-Through-Data-Analysis/blob/main/Dashboard%204.png)

     

5. **Project Insights:**

   - Discovered patterns and trends in customer health and treatment over the years.

   - Identified specific customer segments with high charges, health issues, and frequent treatments.

   - Analyzed the distribution of charges across different tiers and years.

**Conclusion:**

This project provided valuable insights into the medical data through SQL analysis and enhanced data visualization using Power BI. The combination of SQL queries and interactive visualizations enabled a comprehensive understanding of customer health, treatment patterns, and associated charges. This integrated approach is instrumental in deriving actionable insights for medical practitioners and administrators.